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J. Imaging, Volume 9, Issue 6 (June 2023) – 19 articles

Cover Story (view full-size image): Similar to classical watermarking techniques for multimedia content, DNN watermarking should satisfy high robustness against attacks such as fine-tuning, pruning neurons, and overwriting. In this study, we extended the method, encoded by a constant weight code, such that the model can be applied to any convolution layer of the DNN model, and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. The figure displays the statistical distribution of weight parameters in three typical CNN models trained using the ImageNet dataset. According to the characteristics of these distributions, some parameters for embedding a watermark can be determined to consider the trade-off among robustness, transparency, and capacity. View this paper
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23 pages, 6559 KiB  
Article
Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images
by Rihab Hami, Sena Apeke, Pascal Redou, Laurent Gaubert, Ludwig J. Dubois, Philippe Lambin, Dimitris Visvikis and Nicolas Boussion
J. Imaging 2023, 9(6), 124; https://doi.org/10.3390/jimaging9060124 - 20 Jun 2023
Cited by 1 | Viewed by 2637
Abstract
Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between [...] Read more.
Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the “5 Rs” have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included. Full article
(This article belongs to the Topic Medical Image Analysis)
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<p>Simulated oxygen pressure (<span class="html-italic">pO</span><sub>2</sub>) histograms for different vascular fraction values vf (upper: &lt;1%, &lt;3%, &lt;4%; lower: &lt;5%, &lt;8%, ≥8%).</p>
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<p>Different fractionation schedules. Vertical arrows indicate the days when the dose was performed. Four protocols were studied: Standard Protocol (SP), DAHANCA Protocol (DP), Personal Protocol (PP) and CHART Protocol (CP). In the case of CP, three doses were delivered per day (for other details, see the text).</p>
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<p>Evolution of the tumour cell number during the treatment in the case of mouse 1.</p>
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<p>Comparison of simulated and real images for two mice under carbogen (95% oxygen, 5% CO<sub>2</sub>). Input images were acquired before the beginning of radiotherapy. Simulated and real images were obtained after a single irradiation (for other details, see the text).</p>
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<p>Comparison of simulated and real images for two mice under breathing 7% conditions. Input images were acquired before the beginning of radiotherapy. Simulated and real images were obtained after a single irradiation (for other details see text).</p>
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<p>The evolution of normal cell numbers during the treatment. Comparison of the evolution using the LQ Model (blue curve) and without it (red curve) (for other details, see the text).</p>
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<p>Tumour response and cell distribution in the cell cycle. The evolution of the number of tumour cells took into account the distribution in the cell cycle (blue curve) and without (orange curve).</p>
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<p>Tumour response in mice in the presence or absence of variations in radiosensitivity during the cell cycle. (<b>a</b>): Weighting coefficients varied, and in (<b>b</b>), coefficients were constant (for other details, see the text).</p>
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<p>The interactive 3D surface plots displayed a three-dimensional graph of the intensities of voxels. (<b>a</b>): Parameters of radiosensitivity were varied with every irradiation. Of note, for each irradiation per day, a new coefficient was used, and in (<b>b</b>), parameters were fixed (for other details, see the text).</p>
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<p>Percentages of cells in each cell cycle phase (before and after radiotherapy).</p>
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<p>Evolution of percentage of cells in each cell cycle phase over time. The cells were irradiated at t = 20 h.</p>
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<p>Evolution of number of tumour cells with or without tumour repopulation over time.</p>
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<p>The response of tumour cells according to tumour oxygenation.</p>
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<p>Evolution of tumour cell numbers over time according to the <span class="html-italic">α</span>/<span class="html-italic">β</span> ratio.</p>
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<p>Tumour response to radiotherapy for each fractionation protocol using simulated images: (<b>a</b>) Standard Protocol: SP, (<b>b</b>) DAHANCA Protocol: DP, (<b>c</b>) Personal Protocol: PP and (<b>d</b>) CHART Protocol: CP.</p>
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<p>Tumour response to radiotherapy. (<b>a</b>) Evolution of tumour cell numbers over time, and (<b>b</b>) the TCP curve for the simulated tumour. The TCD50 values (in Gy) of the TCP curves are 17.7, 17.9, 20.1 and 23.4 for PP, CP, SP and DP, respectively.</p>
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<p>The evolution of tumour cell numbers over time for a rhabdomyosarcoma tumour based on each protocol: Standard Protocol (SP), DAHANCA Protocol (DP), Personal Protocol (PP) and CHART Protocol (CP).</p>
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<p>TCP curves for each splitting protocol. The values of TCD50 are 13.23, 13.24, 13.72 and 18.18, respectively for CP, PP, DP and SP.</p>
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<p>Tumour images corresponding to the tumour on the seventh day after the start of treatment for each protocol.</p>
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12 pages, 1084 KiB  
Article
Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets
by Armando Barrera-Naranjo, Diana M. Marin-Castrillon, Thomas Decourselle, Siyu Lin, Sarah Leclerc, Marie-Catherine Morgant, Chloé Bernard, Shirley De Oliveira, Arnaud Boucher, Benoit Presles, Olivier Bouchot, Jean-Joseph Christophe and Alain Lalande
J. Imaging 2023, 9(6), 123; https://doi.org/10.3390/jimaging9060123 - 19 Jun 2023
Viewed by 2530
Abstract
A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely [...] Read more.
A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI. Full article
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<p>The discontinuity field on a sagittal 4D flow image during the systolic phase.</p>
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<p>The 3D U-Net architecture used for the deep learning-based segmentation. The architecture was trained using the leave-one-patient-out strategy. The input image for each patient was a volume taken from a magnitude image of the systolic phase. The size of the input volume was set to 146 × 176 × 44. The numbers above the blocks indicate the number of feature maps.</p>
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<p>The examples of the segmentations obtained using the two approaches. The first column presents a case where both methods provided satisfactory results. The second column presents a case where both methods provided poor results. From top to bottom: ground truth data; segmentation using the U-Net network; segmentation based on the level set approach.</p>
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18 pages, 1746 KiB  
Article
A Robust Approach to Multimodal Deepfake Detection
by Davide Salvi, Honggu Liu, Sara Mandelli, Paolo Bestagini, Wenbo Zhou, Weiming Zhang and Stefano Tubaro
J. Imaging 2023, 9(6), 122; https://doi.org/10.3390/jimaging9060122 - 19 Jun 2023
Cited by 19 | Viewed by 7318
Abstract
The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant situations, it is becoming crucial to distinguish [...] Read more.
The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant situations, it is becoming crucial to distinguish between authentic and fake media. Nonetheless, though deepfake generation systems can create convincing images and audio, they may struggle to maintain consistency across different data modalities, such as producing a realistic video sequence where both visual frames and speech are fake and consistent one with the other. Moreover, these systems may not accurately reproduce semantic and timely accurate aspects. All these elements can be exploited to perform a robust detection of fake content. In this paper, we propose a novel approach for detecting deepfake video sequences by leveraging data multimodality. Our method extracts audio-visual features from the input video over time and analyzes them using time-aware neural networks. We exploit both the video and audio modalities to leverage the inconsistencies between and within them, enhancing the final detection performance. The peculiarity of the proposed method is that we never train on multimodal deepfake data, but on disjoint monomodal datasets which contain visual-only or audio-only deepfakes. This frees us from leveraging multimodal datasets during training, which is desirable given their lack in the literature. Moreover, at test time, it allows to evaluate the robustness of our proposed detector on unseen multimodal deepfakes. We test different fusion techniques between data modalities and investigate which one leads to more robust predictions by the developed detectors. Our results indicate that a multimodal approach is more effective than a monomodal one, even if trained on disjoint monomodal datasets. Full article
(This article belongs to the Special Issue Robust Deep Learning Techniques for Multimedia Forensics and Security)
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<p>Proposed pipeline for multimodal deepfake detection.</p>
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<p>Architecture of the classifier <math display="inline"><semantics> <msub> <mi mathvariant="script">C</mi> <mi>m</mi> </msub> </semantics></math>.</p>
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<p>Different fusion strategies considered to perform multimodal deepfake detection.</p>
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<p>Evaluation of the considered detectors on monomodal datasets.</p>
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<p>Evaluation of the considered detectors on multimodal datasets considering different fusion strategies.</p>
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<p>Confusion matrices obtained by considering the <math display="inline"><semantics> <msub> <mi mathvariant="script">D</mi> <mi>LF</mi> </msub> </semantics></math> detector (<span class="html-italic">Late Fusion</span>) on the FakeAVceleb (<b>left</b>), TIMIT (<b>center</b>) and DFDC (<b>right</b>) datasets. The corresponding <math display="inline"><semantics> <msub> <mi>BA</mi> <mrow> <mn>0.5</mn> </mrow> </msub> </semantics></math> values are 0.75, 0.87, and 0.69, respectively.</p>
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<p>Confusion matrices obtained by considering the <math display="inline"><semantics> <msub> <mi mathvariant="script">D</mi> <mi>MF</mi> </msub> </semantics></math> detector (<span class="html-italic">Mid Fusion</span>) on the FakeAVceleb (<b>left</b>), TIMIT (<b>center</b>) and DFDC (<b>right</b>) datasets. The corresponding <math display="inline"><semantics> <msub> <mi>BA</mi> <mrow> <mn>0.5</mn> </mrow> </msub> </semantics></math> values are 0.75, 0.87, and 0.70, respectively.</p>
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<p>Confusion matrices obtained by considering the <math display="inline"><semantics> <msub> <mi mathvariant="script">D</mi> <mi>EF</mi> </msub> </semantics></math> detector (<span class="html-italic">Early Fusion</span>) on the FakeAVceleb (<b>left</b>), TIMIT (<b>center</b>) and DFDC (<b>right</b>) datasets. The corresponding <math display="inline"><semantics> <msub> <mi>BA</mi> <mrow> <mn>0.5</mn> </mrow> </msub> </semantics></math> values are 0.77, 0.88, and 0.74, respectively.</p>
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<p>Evaluation of the considered detectors on multimodal datasets considering monomodal (i.e., visual-only or audio-only) against multimodal approaches.</p>
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<p>Evaluation of the <math display="inline"><semantics> <msub> <mi mathvariant="script">D</mi> <mi>EF</mi> </msub> </semantics></math> detector (<span class="html-italic">Early Fusion</span>) on mixed classes (real audio and fake video and viceversa). The case where both video and audio are fake is excluded.</p>
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13 pages, 1800 KiB  
Article
Live Cell Light Sheet Imaging with Low- and High-Spatial-Coherence Detection Approaches Reveals Spatiotemporal Aspects of Neuronal Signaling
by Mariana Potcoava, Donatella Contini, Zachary Zurawski, Spencer Huynh, Christopher Mann, Jonathan Art and Simon Alford
J. Imaging 2023, 9(6), 121; https://doi.org/10.3390/jimaging9060121 - 16 Jun 2023
Cited by 1 | Viewed by 1728
Abstract
Light sheet microscopy in live cells requires minimal excitation intensity and resolves three-dimensional (3D) information rapidly. Lattice light sheet microscopy (LLSM) works similarly but uses a lattice configuration of Bessel beams to generate a flatter, diffraction-limited z-axis sheet suitable for investigating subcellular compartments, [...] Read more.
Light sheet microscopy in live cells requires minimal excitation intensity and resolves three-dimensional (3D) information rapidly. Lattice light sheet microscopy (LLSM) works similarly but uses a lattice configuration of Bessel beams to generate a flatter, diffraction-limited z-axis sheet suitable for investigating subcellular compartments, with better tissue penetration. We developed a LLSM method for investigating cellular properties of tissue in situ. Neural structures provide an important target. Neurons are complex 3D structures, and signaling between cells and subcellular structures requires high resolution imaging. We developed an LLSM configuration based on the Janelia Research Campus design or in situ recording that allows simultaneous electrophysiological recording. We give examples of using LLSM to assess synaptic function in situ. In presynapses, evoked Ca2+ entry causes vesicle fusion and neurotransmitter release. We demonstrate the use of LLSM to measure stimulus-evoked localized presynaptic Ca2+ entry and track synaptic vesicle recycling. We also demonstrate the resolution of postsynaptic Ca2+ signaling in single synapses. A challenge in 3D imaging is the need to move the emission objective to maintain focus. We have developed an incoherent holographic lattice light-sheet (IHLLS) technique to replace the LLS tube lens with a dual diffractive lens to obtain 3D images of spatially incoherent light diffracted from an object as incoherent holograms. The 3D structure is reproduced within the scanned volume without moving the emission objective. This eliminates mechanical artifacts and improves temporal resolution. We focus on LLS and IHLLS applications and data obtained in neuroscience and emphasize increases in temporal and spatial resolution using these approaches. Full article
(This article belongs to the Special Issue Fluorescence Imaging and Analysis of Cellular System)
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<p>The LLS and IHLLS detection systems. (<b>a</b>) Schematics of both detection systems, LLS (blue arrow) and IHLLS (red arrow). Both systems share the water-immersed microscope objective MO (Nikon 25X, NA 1.1, WD 2 mm, Nikon Corporation, Tokyo, Japan). The LLS system consists of a glass-based tube lens, L<sub>TL</sub> = 500 mm; a Semrock FF01-446/523/600/677-25 bandpass filter, BPF<sub>1</sub> (IDEX Health &amp; Science, West Henrietta, NY, USA); and a Hamamatsu <span class="html-italic">ORCA</span>-Flash 4.0 v3 sCMOS. The diffraction mask in the LLS excitation path was centered for all experiments on an anulus with higher NA, NAout = 0.55 and NAin = 0.485; therefore, the beams used for excitation were more Bessel-like beams. The IHLLS system is equipped with a phase spatial light modulator SLM (1920 × 1152, 9.2 µm pixel size, Meadowlark Inc., Longmont, CO, USA); lenses L<sub>1</sub> = L<sub>4</sub> with focal lengths of 175 mm and L<sub>2</sub> = L<sub>3</sub> with focal lengths of 100 mm; mirrors M<sub>1</sub> (sliding mirror), M<sub>2</sub>, M<sub>3</sub>; polarizer P; band pass filters BPF<sub>2</sub> centered at 575 nm (Chroma Technology Corp., Bellows Falls, VA, USA., 23 nm bandpass width) for the excitation wavelength <math display="inline"><semantics> <mi>λ</mi> </semantics></math> = 488 nm. The camera in the incoherent arm is another Hamamatsu <span class="html-italic">ORCA</span>-Flash 4.0 v3 sCMOS: (<b>b</b>) one diffractive lens of focal length f<sub>SLM</sub> = 415 mm at the phase shift θ<sub>1</sub> = 0 and (<b>c</b>) two diffractive lenses with focal lengths f<sub>d1</sub> = 228 mm and f<sub>d2</sub> = 2444 mm at the phase shift θ<sub>1</sub> = 0. Panels (<b>d</b>,<b>e</b>) show the scanning and detection geometries for the LLS and IHLLS 1L techniques. The vectors represent the x, y, z, and s planes of the Bessel beams that were focused by an excitation objective lens (not showing) to form a lattice light sheet at the sample plan. z and x are moved by the z- and x-galvos. Panels (<b>f</b>,<b>g</b>) show the scanning and detection geometries for the IHLLS 2L technique. While the z-galvo and z-piezo are moved along the <span class="html-italic">z</span> axis to acquire stacks in LLS (<b>d</b>,<b>e</b>), in IHLLS 2L, only the z-galvo is moved at various z positions. The system performances of all three techniques are shown in <a href="#jimaging-09-00121-f002" class="html-fig">Figure 2</a>. The approach schemes for the three experiments are illustrated in panels (<b>h</b>–<b>l</b>). The first three approaches demonstrate the ability to combine LLSM imaging within situ electrophysiology (<b>h</b>–<b>j</b>). (<b>h</b>) Hippocampal neuron imaging demonstrates the ability to resolve very sparse synaptic inputs to the dendrites of pyramidal cells in intact slices in situ. (<b>i</b>) Axons in intact lamprey spinal cord were imaged at high speed (330 frames/second) using Ca<sup>2+</sup> sensitive dye to demonstrate both the spatiotemporal resolution. (<b>j</b>) Lamprey-FM demonstrates the ability to investigate stimulus-evoked lipid vesicle fusion and intracellular transport. We then demonstrated holographic approaches to image in situ lamprey presynaptic structures (<b>k</b>,<b>l</b>). (<b>k</b>) IHLLS 1L, used for settings and calibration, and (<b>l</b>) IHLLS 2L, used for holographic imaging. (<b>a</b>–<b>g</b>) adapted from [<a href="#B16-jimaging-09-00121" class="html-bibr">16</a>].</p>
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<p>Tomographic imaging of 0.5 µm, FOV 208 µm<sup>2</sup>, in a conventional LLS (<b>a</b>) and incoherent LLS with only one diffractive lens (IHLLS 1L) of focal length 400 nm (<b>b</b>), without deconvolution. On the sides and above are shown the max projections through the volume (400 z-galvo steps). The Bessel beams are displayed in the upper left corner of each xy-projection to show the orientation of the beams (FOV 208 µm<sup>2</sup>). The area enclosed inside the colored dashed rectangles are as follows: red—the scanning area for the original LLS (52 µm<sup>2</sup>); yellow—the actual scanning area for the LLS, IHLLS 1L, and IHLLS 2L. The bead in the white dashed rectangle that is in the middle of the lattice sheet is considered when calculating the resolution for the two instruments. The 1D xy and yz sections of the MTFs (<b>c</b>), the 1D xy and yz of the PSFs (<b>d</b>). The FWHM of the curves are cyan, 0.530 µm; magenta, 0.495 µm; red, 0.8341 µm; and blue, 0.9004 µm. IHLLS 2L tomographic beads imaging, (<b>e</b>–<b>i</b>) holograms at −40 μm (<b>e</b>), −30 μm (<b>f</b>), 0 μm (<b>g</b>), +30 μm (<b>h</b>), +40 μm (<b>i</b>), and the z-max projection of all of the best z-reconstructed planes (<b>j</b>). The max projection of the reconstructed volume of the 500 nm beads sample contains the beads localized at z-galvo levels ±40 μm, ±30 μm, and 0 μm. The 1D xy and yz sections of the MTFs of beads #2 and 3 are shown in (<b>k</b>) and the 1D xy and yz of the PSFs of the same beads are shown in (<b>l</b>). The FWHM of the curves in (<b>l</b>) are cyan, 0.4534 µm; magenta, 0.5118 µm; black, 0.7663 µm; and green, 0.7946 µm.</p>
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<p>Resolution of synaptic Ca<sup>2+</sup> responses via Ca<sup>2+</sup>-permeable AMPA receptors in hippocampal CA1 neurons. (<b>a</b>) CA1 hippocampal pyramidal neurons in mouse hippocampal slices in situ were whole cell patch clamped with electrodes containing Alexa 647 hydrazide (200 mM), the Ca<sup>2+</sup>-sensitive dye Oregon Green BAPTA1, and the membrane-impermeant Na<sup>+</sup> channel blocker QX314 to prevent dendritic spiking. Dendrites and dendritic spines were resolved in 3D. (<b>b</b>) The tissue was superfused with the NMDA receptor antagonist D-AP5 (100 mM) and imaged in single LLSM planes at 25 ms frame rates. During recording, a train of 10 stimuli applied via a bipolar stimulating electrode over the Schaffer collateral commissural pathway evoked Ca<sup>2+</sup> transients at discrete sites in the dendritic field, indicated by red arrow heads. (<b>c</b>) Measurements were made of the signals both at sites of activity and between. The discrete nature of the signals and the lack of signal between these sites indicates synaptic Ca<sup>2+</sup> entry. Note that Ca<sup>2+</sup> entry through Ca<sup>2+</sup> permeable AMPA receptors has proven hard to resolve in tissue in situ because of the low incidence and Ca<sup>2+</sup> permeability.</p>
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<p>High-spatiotemporal-resolution imaging of Ca<sup>2+</sup> transients at presynaptic terminals. (<b>a</b>) Lamprey giant axon in the intact spinal cord was recorded with a microelectrode to fill the axon with the Ca<sup>2+</sup>-sensitive dye Oregon Green BAPTA1. The axon was imaged at 3 ms intervals in a single LLSM plane and single action potential stimulated at 0 ms. In this example, the site of Ca<sup>2+</sup> entry was in the plane of the LLSM (white arrows), although others were just out of focus (e.g., red arrow). The raw data with no dithering are shown (top, grey), and data are expressed as ΔF/F (in LUT colors, second row) for frames before and during the response. (<b>b</b>) Three superposed example traces showing the extraordinary resolution of these responses that are caused by between 1 and 6 voltage-dependent Ca<sup>2+</sup> channels [<a href="#B6-jimaging-09-00121" class="html-bibr">6</a>]. (<b>c</b>) The LLSM causes very little photodamage. Shown are the amplitudes of 80 sequential responses captured at 1 min intervals. Prior experiments using epifluorescence or confocal imaging of these responses cause substantial loss of signal due to photo-damage after 20 recorded responses [<a href="#B8-jimaging-09-00121" class="html-bibr">8</a>].</p>
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<p>Vesicle staining and stimulus-evoked destaining in giant axons. (<b>a</b>) Lamprey giant axon in the intact spinal cord was recorded with a microelectrode containing Alexa fluor 647 hydrazide (green, excited with 640 nm excitation) visualized under LLSM. The axon was filled by pressure injection. During superfusion of the tissue with the dye FM1-43 (red), the axon was stimulated with 2000 action potentials via a depolarizing pulse applied through the microelectrode. After stimulation, excess FM1-43 was cleared from the tissue with Advasep-7 [<a href="#B21-jimaging-09-00121" class="html-bibr">21</a>,<a href="#B22-jimaging-09-00121" class="html-bibr">22</a>]. These revealed puncta imaged at 488 nm excitation around the surface of the axon shown in 3D (left) and for 3 planes (right). (<b>b</b>) The axon was stimulated (5 Hz, 2000 action potentials, 10 overlaid example traces shown, black), and the 50 planes at 1 mm z plane steps were captured continuously at 20 ms intervals. Shown on the left is a single plane before stimulation, highlighting 2 puncta. Graph (red) shows destaining during stimuli for these two puncta, labeled a and b. The right-hand image shows this plane after stimuli. (<b>c</b>) This panel emphasizes the spatio-temporal resolution of this imaging approach. Prior use of confocal microscopy demonstrated destaining, but during operation, LLSM reveals numerous components of puncta exiting the structures and being transported both anterogradely (example shown with white arrows) and retrogradely in the axon. Prior approaches had neither sufficient temporal resolution to track objects in 3D nor the sensitivity to resolve these objects.</p>
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<p>(<b>a</b>) Tomographic imaging of a lamprey spinal cord ventral horn neuron with axons (<span class="html-italic">xy, FOV</span> 208 × 208 µm<sup>2</sup>, 2048 × 2048 pixels; <span class="html-italic">yz, (xz) FOV</span> 208 × 30 µm<sup>2</sup>, 2048 × 300 pixels) in a conventional LLS and IHLLS 1L without deconvolution. On the sides and above are shown the max projections through the volume (300 z-galvo steps). The Bessel beams are displayed in the upper-left corner of the xy-projection to show the orientation of the beams (<span class="html-italic">FOV</span> 208 µm<sup>2</sup>). The area enclosed inside the red dashed rectangles is the scanning area for the original LLS (52 µm<sup>2</sup>). (<b>b</b>) Tomographic imaging of a lamprey spinal cord ventral horn neuron with axons (<span class="html-italic">xy, FOV</span> 208 × 208 µm<sup>2</sup>, 2048 × 2048 pixels; <span class="html-italic">yz, (xz) FOV</span> 208 × 60 µm<sup>2</sup>, 2048 × 600 pixels) in IHLLS 2L without deconvolution. On the sides and above are shown the max projections through the volume (in this case, 5 z-galvo steps). The Bessel beams are displayed in the upper-left corner of the xy-projection to show the orientation of the beams, including some diffraction patterns caused by the phased SLM. The area enclosed inside the yellow dashed rectangles is the scanning area for the IHLLS 2L (208 µm<sup>2</sup>).</p>
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18 pages, 14851 KiB  
Article
A Computational Approach to Hand Pose Recognition in Early Modern Paintings
by Valentine Bernasconi, Eva Cetinić and Leonardo Impett
J. Imaging 2023, 9(6), 120; https://doi.org/10.3390/jimaging9060120 - 15 Jun 2023
Cited by 5 | Viewed by 2890
Abstract
Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual [...] Read more.
Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual art, a comprehensive terminology for the classification of depicted hand poses is still lacking. In this article, we present the process of creating a new annotated dataset of pictorial hand poses. The dataset is based on a collection of European early modern paintings, from which hands are extracted using human pose estimation (HPE) methods. The hand images are then manually annotated based on art historical categorization schemes. From this categorization, we introduce a new classification task and perform a series of experiments using different types of features, including our newly introduced 2D hand keypoint features, as well as existing neural network-based features. This classification task represents a new and complex challenge due to the subtle and contextually dependent differences between depicted hands. The presented computational approach to hand pose recognition in paintings represents an initial attempt to tackle this challenge, which could potentially advance the use of HPE methods on paintings, as well as foster new research on the understanding of hand gestures in art. Full article
(This article belongs to the Special Issue Pattern Recognition Systems for Cultural Heritage)
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<p>Example images of paintings from the source data collection, the photographic Collection of the Bibliotheca Hertziana, Max Planck Institute for Art History. Reprinted with permission from Bibliotheca Hertziana, Max Planck Institute for Art History in Rome. 2023.</p>
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<p>Sample of pictures representing an issue for automated hand detection with OpenPose. Reprinted with permission from Bibliotheca Hertziana, Max Planck Institute for Art History in Rome. 2023.</p>
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<p>Illustration of the hand extraction process using the pretrained OpenPose model on one artwork image example.</p>
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<p>Nine different hand categories based on the chirograms defined by Dimova.</p>
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<p>Category-based distribution of images in the Painted Hand Pose dataset.</p>
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<p>Hand pose representations illustrating the positions of the 21 main OpenPose hand keypoints, which serve as a basis for our hand feature descriptions based on the 19 angles and 20 unit vectors. (<b>a</b>) The 21 hand keypoints, (<b>b</b>) the 19 hand angles in red, and (<b>c</b>) the 20 hand unit vectors in blue.</p>
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<p>T-SNE projection of the KP features and indication of whether the left or right hand was used.</p>
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<p>Left and right hand distribution among the categories.</p>
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<p>Classification accuracy of different classifiers and features.</p>
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<p>Confusion matrix from the MLP classifier on KP features.</p>
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<p>The F1 score value in relation to the number of images in the per-class test set and the different feature types obtained when using the MLP classifier.</p>
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<p>Example images that belong to different classes but depict hands in similar poses.</p>
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<p>Gestures for Artworks Browsing application. (<b>a</b>) Input: real-time recording of the hand, (<b>b</b>) Output: similarly painted hand images and their source artworks. Screenshot of the Gestures for Artworks Browsing application.</p>
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<p>Interactive interface from the art installation <span class="html-italic">La main baladeuse</span>, with the hand of the user represented as a skeleton in the center.</p>
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13 pages, 711 KiB  
Article
Digital Breast Tomosynthesis: Towards Dose Reduction through Image Quality Improvement
by Ana M. Mota, João Mendes and Nuno Matela
J. Imaging 2023, 9(6), 119; https://doi.org/10.3390/jimaging9060119 - 11 Jun 2023
Cited by 3 | Viewed by 2182
Abstract
Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image quality improvement provided by DBT is accompanied by [...] Read more.
Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image quality improvement provided by DBT is accompanied by an increase in the radiation dose for the patient. Here, a method based on 2D Total Variation (2D TV) minimization to improve image quality without the need to increase the dose was proposed. Two phantoms were used to acquire data at different dose ranges (0.88–2.19 mGy for Gammex 156 and 0.65–1.71 mGy for our phantom). A 2D TV minimization filter was applied to the data, and the image quality was assessed through contrast-to-noise ratio (CNR) and the detectability index of lesions before and after filtering. The results showed a decrease in 2D TV values after filtering, with variations of up to 31%, increasing image quality. The increase in CNR values after filtering showed that it is possible to use lower doses (−26%, on average) without compromising on image quality. The detectability index had substantial increases (up to 14%), especially in smaller lesions. So, not only did the proposed approach allow for the enhancement of image quality without increasing the dose, but it also improved the chances of detecting small lesions that could be overlooked. Full article
(This article belongs to the Section Medical Imaging)
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<p>Acrylic phantom simulating breast tissue and lesions of high attenuation (aluminum disks of different diameters and 1 mm thickness). Diameters of the disks of the first column (<b>top to bottom</b>): 5.0 mm, 4.0 mm, 3.0 mm, 2.0 mm, 1.0 mm, and 0.5 mm, respectively; Diameter of the disks of the second column (<b>top to bottom</b>): 4.0 mm, 2.0 mm, 0.5 mm, 1.0 mm, 3.0 mm, and 5.0 mm, respectively.</p>
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<p>ROIs used to calculate CNR using the Gammex 156 phantom. Circular ROI over the 2 mm lesion-like mass and square background ROI centered in the lesion, excluding all voxels corresponding to the lesion.</p>
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<p>(<b>a</b>) ROI used to compute the task-based transfer function (TTF). (<b>b</b>) ROIs used for the noise power spectrum (NPS) assessment.</p>
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<p>The <b>top row</b> shows the synthesized signals of four different sizes to be detected with a designer contrast profile, and the <b>bottom row</b>, with a rectangular contrast profile. The Fourier transform of such a signal is the task function, <math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>s</mi> <mi>k</mi> </mrow> </msub> </semantics></math>.</p>
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<p>Values of CNR obtained for the original data and the filtered data of the Gammex 156 phantom as a function of dose. The purple arrows represents the possible dose reduction made by applying the filter to obtain the same CNR.</p>
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<p>Images of a 2.0 mm mass of the Gammex 156 phantom obtained for the original (<b>up row</b>) and filtered data (<b>down row</b>) for each acquisition dose.</p>
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<p>Images of a cluster of microcalcifications of the Gammex 156 phantom obtained for the original (<b>up row</b>) and filtered data (<b>down row</b>) for each acquisition dose.</p>
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<p>Detectability index values obtained for a circular signal with designer profile and diameters of (<b>a</b>) 5 mm, (<b>b</b>) 3 mm, (<b>c</b>) 1 mm, and (<b>d</b>) 0.5 mm for each acquisition dose of original and filtered data. The purple arrows represents the possible dose reduction made by applying the filter to obtain the same detectability index.</p>
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<p>Detectability index values obtained for a circular signal with rectangular profile and diameters of (<b>a</b>) 5 mm, (<b>b</b>) 3 mm, (<b>c</b>) 1 mm, and (<b>d</b>) 0.5 mm for each acquisition dose of original and filtered data. The purple arrows represent possible dose reduction made by applying the filter to obtain the same detectability index.</p>
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11 pages, 2684 KiB  
Article
Short-Term Precision and Repeatability of Radiofrequency Echographic Multi Spectrometry (REMS) on Lumbar Spine and Proximal Femur: An In Vivo Study
by Carmelo Messina, Salvatore Gitto, Roberta Colombo, Stefano Fusco, Giada Guagliardo, Mattia Piazza, Jacopo Carlo Poli, Domenico Albano and Luca Maria Sconfienza
J. Imaging 2023, 9(6), 118; https://doi.org/10.3390/jimaging9060118 - 11 Jun 2023
Cited by 6 | Viewed by 1719
Abstract
To determine the short-term intra-operator precision and inter-operator repeatability of radiofrequency echographic multi-spectrometry (REMS) at the lumbar spine (LS) and proximal femur (FEM). All patients underwent an ultrasound scan of the LS and FEM. Both precision and repeatability, expressed as root-mean-square coefficient of [...] Read more.
To determine the short-term intra-operator precision and inter-operator repeatability of radiofrequency echographic multi-spectrometry (REMS) at the lumbar spine (LS) and proximal femur (FEM). All patients underwent an ultrasound scan of the LS and FEM. Both precision and repeatability, expressed as root-mean-square coefficient of variation (RMS-CV) and least significant change (LSC) were obtained using data from two consecutive REMS acquisitions by the same operator or two different operators, respectively. The precision was also assessed in the cohort stratified according to BMI classification. The mean (±SD) age of our subjects was 48.9 ± 6.8 for LS and 48.3 ± 6.1 for FEM. Precision was assessed on 42 subjects at LS and 37 subjects on FEM. Mean (±SD) BMI was 24.71 ± 4.2 for LS and 25.0 ± 4.84 for FEM. Respectively, the intra-operator precision error (RMS-CV) and LSC resulted in 0.47% and 1.29% at the spine and 0.32% and 0.89% at the proximal femur evaluation. The inter-operator variability investigated at the LS yielded an RMS-CV error of 0.55% and LSC of 1.52%, whereas for the FEM, the RMS-CV was 0.51% and the LSC was 1.40%. Similar values were found when subjects were divided into BMI subgroups. REMS technique provides a precise estimation of the US-BMD independent of subjects’ BMI differences. Full article
(This article belongs to the Section Medical Imaging)
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<p>Examples of how REMS data deriving from femoral neck scans are analyzed to create a patient-specific spectrum, which is compared with spectral reference models. Regarding the spectra images, amplitude [dB] is on the y-axis, while frequency [MHz] is on the x-axis.</p>
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<p>EchoStation device. Schematic representation of the EchoStation ultrasound machine, provided with the main unit EchoS, probe, and panel PC that implements REMS Technology.</p>
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<p>REMS acquisitions at the spine (<b>a</b>) and proximal femur (<b>b</b>). The figure depicts (upper panel) the medical report and (lower panel) a typical echographic image with the identification of the ROI (blue) and the bone interfaces (green). ROI, region of interest.</p>
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<p>Study flowchart showing inclusion/exclusion criteria, the total number of women enrolled, the total scan analyzed after excluding erroneous acquisitions, and final BMI stratification. BMI = Body Mass Index; LS = lumbar spine; FEM = proximal femur; ROI = region of interest.</p>
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19 pages, 776 KiB  
Article
White Box Watermarking for Convolution Layers in Fine-Tuning Model Using the Constant Weight Code
by Minoru Kuribayashi, Tatsuya Yasui and Asad Malik
J. Imaging 2023, 9(6), 117; https://doi.org/10.3390/jimaging9060117 - 9 Jun 2023
Cited by 4 | Viewed by 1807
Abstract
Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, transparency, and other factors. Studies have focused on robustness [...] Read more.
Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created. Full article
(This article belongs to the Special Issue Robust Deep Learning Techniques for Multimedia Forensics and Security)
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<p>Parameter space in a DNN model. The closest local minimum is selected in a training phase from a given initial point.</p>
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<p>Probability density function of selected weight parameters.</p>
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<p>Comparison of the histogram of weight parameters in various convolution layers in pretrained CNN models.</p>
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<p>Procedure of calculating a threshold <math display="inline"><semantics> <msub> <mi>T</mi> <mn>1</mn> </msub> </semantics></math> from the <span class="html-italic">t</span>-th convolution layer of a DNN model.</p>
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<p>Comparison of accuracy and loss when watermark encoded by <math display="inline"><semantics> <mi>CWC</mi> </semantics></math>(32, 3307) is embedded into each layer, where “org” denotes the original fine-tuning model.</p>
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<p>Comparison of accuracy and loss when watermark is embedded into each layer, where the red line denotes the case of original fine-tuning model.</p>
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<p>Comparison of <math display="inline"><semantics> <mover accent="true"> <mrow> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> <mo stretchy="false">˜</mo> </mover> </semantics></math> in various convolution layers, where watermark is encoded by CWC(32, 3307).</p>
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19 pages, 570 KiB  
Article
An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment
by Domonkos Varga
J. Imaging 2023, 9(6), 116; https://doi.org/10.3390/jimaging9060116 - 8 Jun 2023
Cited by 1 | Viewed by 1689
Abstract
Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework [...] Read more.
Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework for FR-IQA that combines multiple metrics and tries to leverage the strength of each by formulating FR-IQA as an optimization problem. Following the idea of other fusion-based metrics, the perceptual quality of a test image is defined as the weighted product of several already existing, hand-crafted FR-IQA metrics. Unlike other methods, the weights are determined in an optimization-based framework and the objective function is defined to maximize the correlation and minimize the root mean square error between the predicted and ground-truth quality scores. The obtained metrics are evaluated on four popular benchmark IQA databases and compared to the state of the art. This comparison has revealed that the compiled fusion-based metrics are able to outperform other competing algorithms, including deep learning-based ones. Full article
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<p>Twenty percent of the reference images with their corresponding distorted counterparts are selected to determine the parameters of the proposed fusion-based metric in the optimization process. The resulting metric is codenamed as OFIQA.</p>
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<p>The weighted product of the selected FR-IQA metrics is used to estimate the perceptual quality of a distorted image in the evaluation stage.</p>
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<p>RMSE measured on LIVE [<a href="#B4-jimaging-09-00116" class="html-bibr">4</a>].</p>
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<p>RMSE measured on TID2013 [<a href="#B76-jimaging-09-00116" class="html-bibr">76</a>].</p>
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<p>RMSE measured on TID2008 [<a href="#B77-jimaging-09-00116" class="html-bibr">77</a>].</p>
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<p>RMSE measured on CSIQ [<a href="#B33-jimaging-09-00116" class="html-bibr">33</a>].</p>
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34 pages, 6681 KiB  
Review
Imaging of Gastrointestinal Tract Ailments
by Boyang Sun, Jingang Liu, Silu Li, Jonathan F. Lovell and Yumiao Zhang
J. Imaging 2023, 9(6), 115; https://doi.org/10.3390/jimaging9060115 - 8 Jun 2023
Cited by 3 | Viewed by 7077
Abstract
Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management [...] Read more.
Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel’s diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments. Full article
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<p>Immuno-PET images of colons, ceca, and mesenteric lymph nodes in mice. Reprinted with the permission of [<a href="#B50-jimaging-09-00115" class="html-bibr">50</a>].</p>
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<p>Diagnosis of inflammatory Crohn’s disease with extraenteric complications by computed tomography enterography. (<b>A</b>) Multifocal segmental stricture showing wall thickening, mural hyperenhancement, and stratification shown by arrows and engorged vasa recta, comb sign highlighted by a circle. (<b>B</b>) Mesenteric abscess is shown by arrowheads. (<b>C</b>) Adjacent to the enteroenteric fistula shown by the arrow. Reprinted with permission of [<a href="#B57-jimaging-09-00115" class="html-bibr">57</a>].</p>
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<p>An oblique axial T2w fat-sat (<b>A</b>) and T2w (<b>B</b>) images show a complex transsphinteric fistulous tract with a “horseshoe” feature. Reprinted with permission of [<a href="#B87-jimaging-09-00115" class="html-bibr">87</a>].</p>
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<p>Computed tomography findings of appendicitis. (<b>A</b>) Axial CT image showing an enlarged, fluid-filled appendix (arrowheads). (<b>B</b>) Axial CT image showing edema of the cecal tip with oral contrast pointing (arrow) towards the base of the inflamed appendix. Axial CT images in the same patient (<b>C</b>) before and (<b>D</b>) after intravenous contrast show a thickened appendix with submucosal edema. Reprinted with permission of [<a href="#B138-jimaging-09-00115" class="html-bibr">138</a>].</p>
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<p>A patient with extensive metastatic disease of an intestinal carcinoid tumor. (<b>A</b>) Automatic fused image of PET/CT. (<b>B</b>) A venous-dominant contrastenhanced CT scan. (<b>C</b>) T2-weighted TSE image of MRI. (<b>D</b>) A manual fused image of PET/MRI. (<b>E</b>) with multiple hepatic metastases. PET, CT and the overall view of all MRI sequences identified a great cyst in the right lobe of the liver (arrow). Reprinted with the permission of [<a href="#B170-jimaging-09-00115" class="html-bibr">170</a>].</p>
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<p>Spot radiograph of the middle of the transverse colon obtained from a patient near-erect position. The interhaustral folds are straight; a representative fold is identified with an arrow. The haustral sacculations are distended, but not overdistended and flattened. Reprinted with permission of [<a href="#B135-jimaging-09-00115" class="html-bibr">135</a>].</p>
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<p>Sagittal (<b>A</b>) and axial (<b>B</b>) oblique contrast-enhanced 3D volume-rendered CT scans revealed a round exophytic mass in the stomach, 5-cm exophytic mass (arrows) that arises from the stomach, which proved to be a benign GIST during surgery. Reprinted with the permission of [<a href="#B183-jimaging-09-00115" class="html-bibr">183</a>].</p>
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<p>(<b>A</b>) Meckel’s diverticulum was diagnosed by contrast-enhanced CT scan indicated by a straight arrow containing air and fluid located in the right paracolic gutter anterior to ascending colon marked by C. Ectopic pancreas was marked by the curved arrow. (<b>B</b>) Photograph of the gross pathology specimen demonstrated Meckel’s diverticulum marked by a straight arrow from adjacent ileum marked by i. Solid nodule corresponds to heterotopic pancreatic tissue (shown by curved arrow) that produces adjacent fat. The increments on the ruler are in centimeters. Reprinted with the permission of [<a href="#B221-jimaging-09-00115" class="html-bibr">221</a>].</p>
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<p>Positron emission tomography (PET) and photoacoustic tomography (PAT) imaging of intestine in mice. (<b>A</b>) PET image of intestine using nanonap radio-labeled by <sup>64</sup>Cu as contrast agent. (<b>B</b>) PAT as a diagnostic tool for small bowel obstruction. Reproduced with permission of [<a href="#B14-jimaging-09-00115" class="html-bibr">14</a>].</p>
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<p>Normal defecography study showing that the anal canal is open marked by a black arrow and has descended below ischial tuberosities (lower white arrow), and contrast-filled small bowel is seen within the pelvis (upper white arrow). Reprinted with the permission of [<a href="#B250-jimaging-09-00115" class="html-bibr">250</a>].</p>
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<p>Representative CT imaging for constipation diagnosis. (<b>A</b>) CT image was used to rate the stool volume as 4 and the gas volume as 2 (red circle). (a) Ascending colon. (<b>B</b>) CT image was used to rate the stool volume as 1 and the gas volume as 2 (red circle). (sf), splenic flexure. (<b>C</b>) CT image was used to rate the stool volume as 3 and the gas volume as 2 (red circle). (d) Descending colon. (<b>D</b>) CT image was used to rate the stool volume as 3 and the gas volume as 3 (red circle). (re), rectum. Reprinted with the permission of [<a href="#B261-jimaging-09-00115" class="html-bibr">261</a>].</p>
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12 pages, 14005 KiB  
Article
Clinical Utility of 18Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) in Multivisceral Transplant Patients
by Shao Jin Ong, Lisa M. Sharkey, Kai En Low, Heok K. Cheow, Andrew J. Butler and John R. Buscombe
J. Imaging 2023, 9(6), 114; https://doi.org/10.3390/jimaging9060114 - 7 Jun 2023
Viewed by 1342
Abstract
Multivisceral transplant (MVTx) refers to a composite graft from a cadaveric donor, which often includes the liver, the pancreaticoduodenal complex, and small intestine transplanted en bloc. It remains rare and is performed in specialist centres. Post-transplant complications are reported at a higher rate [...] Read more.
Multivisceral transplant (MVTx) refers to a composite graft from a cadaveric donor, which often includes the liver, the pancreaticoduodenal complex, and small intestine transplanted en bloc. It remains rare and is performed in specialist centres. Post-transplant complications are reported at a higher rate in multivisceral transplants because of the high levels of immunosuppression used to prevent rejection of the highly immunogenic intestine. In this study, we analyzed the clinical utility of 28 18F-FDG PET/CT scans in 20 multivisceral transplant recipients in whom previous non-functional imaging was deemed clinically inconclusive. The results were compared with histopathological and clinical follow-up data. In our study, the accuracy of 18F-FDG PET/CT was determined as 66.7%, where a final diagnosis was confirmed clinically or via pathology. Of the 28 scans, 24 scans (85.7%) directly affected patient management, of which 9 were related to starting of new treatments and 6 resulted in an ongoing treatment or planned surgery being stopped. This study demonstrates that 18F-FDG PET/CT is a promising technique in identifying life-threatening pathologies in this complex group of patients. It would appear that 18F-FDG PET/CT has a good level of accuracy, including for those MVTx patients suffering from infection, post-transplant lymphoproliferative disease, and malignancy. Full article
(This article belongs to the Section Medical Imaging)
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<p>The patient presented with an elevated white cell count and raised C-reactive protein (CRP) of greater than 250 mg/L, but multiple previous image-guided drains, aspirations, line and blood cultures were negative. <sup>18</sup>F-FDG PET/CT localised the infective focus to the left peripherally inserted central catheter (PICC) line (white arrow). The PICC line was removed and there was a subsequent improvement in the patient’s clinical condition.</p>
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<p>The patient presented with 1 week of pyrexia but no source of infection was identified. Increased uptake was demonstrated in the transplanted stomach (white arrows, image (<b>A</b>)) and small bowel (white arrows, image (<b>B</b>)) suggestive of rejection. Biopsy just prior to <sup>18</sup>F-FDG PET/CT demonstrated a non-specific increase in apoptotic debris within the lamina propria of uncertain significance. As no focus of infection was demonstrated and the appearance of the transplanted stomach (<b>A</b>) and small bowel (<b>B</b>) was suggestive of rejection, the patient was treated for rejection with immune suppression. Following anti-rejection treatment, the patient’s clinical picture improved with resolution of the pyrexia. Subsequent biopsies taken 6 weeks later demonstrated no remaining features of rejection.</p>
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<p>The patient presented with continued pyrexia and a white cell count of 40 × 10<sup>9</sup>cells/L. CT from 4 days prior demonstrated a lesser sac collection (<b>A</b>) with clinical concerns that the lesser sac collection (white arrows, image (<b>A</b>)) may be the source of sepsis and the permacol gusset mesh used for closure may be infected. <sup>18</sup>F-FDG PET/CT (<b>B</b>,<b>C</b>) demonstrated the collection in the lesser sac (white arrows, image (<b>B</b>)) is likely to be innocuous as it did not show high tracer uptake and there was no significant uptake at the site of the permacol gusset closure material (short arrows, image (<b>C</b>)) to suggest an infected implant. The patient avoided a repeat operation for a surgical washout of the lesser sac collection and the removal of the closure mesh.</p>
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<p>The patient was admitted with an elevated amylase level 6 years post multivisceral transplant. CT demonstrated increased prominence of mesenteric lymph nodes and there was a clinical suspicion of PTLD. <sup>18</sup>F-FDG PET/CT demonstrated multiple FDG avid (SUVmax 12.6) mesenteric lymph nodes (white arrows)(<b>A</b>–<b>C</b>). Biopsy demonstrated a monomorphic plasma cell infiltration in keeping with PTLD. Anti-rejection/immunosuppressant therapy was reduced in an attempt to control the PTLD. Follow-up <sup>18</sup>F-FDG PET/CT examination was performed at 2.5 months (<b>D</b>,<b>E</b>) due to an inability to biopsy the deep-seated lymph node in a difficult abdomen with significant thrombocytopenia. This showed a marked increase in SUV (SUVmax 23.7) and the size of the mesenteric lymph nodes, a large FDG avid para-aortic lymph node (white arrow with adjacent red crosshairs, image (<b>E</b>)) and additional involvement of cervical lymph nodes (white arrows, image (<b>D</b>)) with SUVmax at 4.6, in keeping with progressive PTLD. Results of this examination were discussed at MDT and Rituximab was commenced. A further follow-up <sup>18</sup>F-FDG PET/CT was performed 10 weeks after the initiation of Rituxiamb and demonstrated resolution with a significant drop in SUV (<b>F</b>) for the reference nodes.</p>
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<p>CT performed on day 19 post transplant for sepsis and query ischemia or rejection. Portal venous-phase CT imaging was performed on day 19 post transplant (<b>A</b>) demonstrating subtle mural oedema in the caecum (arrowhead) and sub centimeter mesenteric lymph nodes (arrows) surrounding the caecum. Maximum intensity projection (<b>B</b>) of the superior mesenteric origin on post-operative day 19 CT demonstrates a double kink (arrows) at this site. The patient subsequently underwent angiography and pressure measurements, which demonstrated a 27 mmHg pressure gradient between the aorta and SMA. Angioplasty was performed and reduced the pressure gradient to 21 mmHg. Attenuation-corrected <sup>18</sup>F-FDG PET (<b>C</b>) demonstrated increased uptake in the mucosa of the small bowel surrounding the stoma and the mesenteric lymph nodes. Hybrid imaging <sup>18</sup>F-FDG PET-CT demonstrated co-localisation of the increased uptake to the small bowel surrounding the stoma and also at the mesenteric lymph nodes (<b>D</b>). Biopsies performed on the small bowel surrounding the stoma were reported to be in keeping with mild rejection. The increased low-grade uptake in the stomach (<b>E</b>) was interpreted as physiological uptake.</p>
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21 pages, 14377 KiB  
Article
An Enhanced Photogrammetric Approach for the Underwater Surveying of the Posidonia Meadow Structure in the Spiaggia Nera Area of Maratea
by Francesca Russo, Silvio Del Pizzo, Fabiana Di Ciaccio and Salvatore Troisi
J. Imaging 2023, 9(6), 113; https://doi.org/10.3390/jimaging9060113 - 31 May 2023
Cited by 1 | Viewed by 1801
Abstract
The Posidonia oceanica meadows represent a fundamental biological indicator for the assessment of the marine ecosystem’s state of health. They also play an essential role in the conservation of coastal morphology. The composition, extent, and structure of the meadows are conditioned by the [...] Read more.
The Posidonia oceanica meadows represent a fundamental biological indicator for the assessment of the marine ecosystem’s state of health. They also play an essential role in the conservation of coastal morphology. The composition, extent, and structure of the meadows are conditioned by the biological characteristics of the plant itself and by the environmental setting, considering the type and nature of the substrate, the geomorphology of the seabed, the hydrodynamics, the depth, the light availability, the sedimentation speed, etc. In this work, we present a methodology for the effective monitoring and mapping of the Posidonia oceanica meadows by means of underwater photogrammetry. To reduce the effect of environmental factors on the underwater images (e.g., the bluish or greenish effects), the workflow is enhanced through the application of two different algorithms. The 3D point cloud obtained using the restored images allowed for a better categorization of a wider area than the one made using the original image elaboration. Therefore, this work aims at presenting a photogrammetric approach for the rapid and reliable characterization of the seabed, with particular reference to the Posidonia coverage. Full article
(This article belongs to the Section Image and Video Processing)
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<p><span class="html-italic">Posidonia oceanica</span> meadows, Maratea area: characterization of the seabed, digital terrain model (DTM) (<b>a</b>), and side-scan sonar photo-mosaic processing (<b>b</b>).</p>
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<p>Photogrammetric workflow, the diagram illustrates the process adopted in this work.</p>
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<p>Radial distortion plot (<b>a</b>) and residual (<b>b</b>) on the image sensor obtained at the end of the on-job camera calibration process.</p>
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<p>Underwater optical imaging process and the selective attenuation of light (adapted from [<a href="#B49-jimaging-09-00113" class="html-bibr">49</a>]).</p>
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<p>Section of the points cloud (<b>a</b>) and corresponding frame (<b>b</b>).</p>
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<p>Three-dimensional model of the SN2 transect showing the measure of the vertical profile of the matte.</p>
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<p>Characterization of the SN2 (<b>a</b>) and ST7 (<b>b</b>) transects on the basis of the identified layers. The targets are depicted in red.</p>
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<p>A comparison of the two different enhancement algorithms: (<b>a</b>) the original image; (<b>b</b>) the frame processed with IBLA; and (<b>c</b>) the frame processed using CNN. The corresponding histograms reported the variation of the blue channel with respect to the original image (<b>d</b>), IBLA (<b>e</b>), and CNN (<b>f</b>). For each histogram, the median value was reported and illustrated with a vertical red line.</p>
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<p>SN2 transect—comparison between the frame GH1049_2605_1347_04_frame00345_512 before (<b>a</b>) and after (<b>b</b>) the enhancement.</p>
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<p>ST7 transect—comparison between the same frame GOPR0404_frame00227_512 before (<b>a</b>) and after (<b>b</b>) the enhancement.</p>
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<p>Three-dimensional model of the SN2 transect after the application of the CNN-DL algorithm.</p>
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<p>Three-dimensional model of the ST7 transect after the application of the CNN-DL algorithm.</p>
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<p>SN2 transect—comparison between a detail of the 3D model before (<b>a</b>) and after (<b>b</b>) the enhancement.</p>
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<p>ST7 transect—comparison between a detail of the 3D model before (<b>a</b>) and after (<b>b</b>) the enhancement.</p>
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<p>SN2 transect—identification of the habitat from the 3D model obtained from the enhanced frames processing (<b>a</b>): sea bottom coverage of Posidonia (<b>b</b>), matte (<b>c</b>), and sand (<b>d</b>).</p>
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<p>ST7 transect—identification of the habitat from the 3D model obtained from the enhanced frame processing (<b>a</b>): sea bottom coverage of Posidonia (<b>b</b>), matte (<b>c</b>), sand (<b>d</b>), and rocks (<b>e</b>).</p>
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11 pages, 33561 KiB  
Article
Terahertz Constant Velocity Flying Spot for 3D Tomographic Imaging
by Abderezak Aouali, Stéphane Chevalier, Alain Sommier and Christophe Pradere
J. Imaging 2023, 9(6), 112; https://doi.org/10.3390/jimaging9060112 - 31 May 2023
Cited by 1 | Viewed by 1415
Abstract
This work reports on a terahertz tomography technique using constant velocity flying spot scanning as illumination. This technique is essentially based on the combination of a hyperspectral thermoconverter and an infrared camera used as a sensor, a source of terahertz radiation held on [...] Read more.
This work reports on a terahertz tomography technique using constant velocity flying spot scanning as illumination. This technique is essentially based on the combination of a hyperspectral thermoconverter and an infrared camera used as a sensor, a source of terahertz radiation held on a translation scanner, and a vial of hydroalcoholic gel used as a sample and mounted on a rotating stage for the measurement of its absorbance at several angular positions. From the projections made in 2.5 h and expressed in terms of sinograms, the 3D volume of the absorption coefficient of the vial is reconstructed by a back-projection method based on the inverse Radon transform. This result confirms that this technique is usable on samples of complex and nonaxisymmetric shapes; moreover, it allows 3D qualitative chemical information with a possible phase separation in the terahertz spectral range to be obtained in heterogeneous and complex semitransparent media. Full article
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<p>Experimental setup and a priori numerical processing of the acquisitions: (<b>a</b>) complete description of the experimental setup dedicated to 3D terahertz imaging; (<b>b</b>) schematic of the numerical processing necessary on each acquired film to obtain a useful image.</p>
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<p>Measurement procedure and reconstruction of the sample sections: (<b>a</b>) visible image of the hydroalcoholic gel vial used; (<b>b</b>) images of the absorbance of the hydroalcoholic gel vial as a function of different rotation angles; (<b>c</b>) representation of the normalised sinograms at different “z” positions and as a function of the rotation angles; (<b>d</b>) representation of the normalized reconstructed sections at different “z” positions of the sample using the filtered back-projection method.</p>
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<p>3D terahertz tomography of the normalised absorption coefficient obtained using a hydroalcoholic gel vial (half-filled with hydroalcoholic gel) as a sample: (<b>a</b>) photograph of the hydroalcoholic gel vial in the visible light; (<b>b</b>) external perspective view of the 3D absorption coefficient of the vial; (<b>c</b>) perspective view from the midplane of the 3D absorption coefficient of the vial; (<b>d</b>) perspective view from the midplane of the 3D absorption coefficient of the vial with segmentation of the absorption levels; (<b>e</b>) representation of different internal planes of the 3D absorption coefficient by scanning along the <span class="html-italic">y</span>-axis; (<b>f</b>) histogram obtained from the midplane of the segmented 3D absorption coefficient of the vial.</p>
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<p>Spatial effects of the applied imaging method: (<b>a</b>) spatial resolution on a reconstructed section of the vial; (<b>b</b>) spatial derivation applied to the Gaussian-shaped THz beam; (<b>c</b>) effect of the spatial derivative filter in artificially eliminating optical diffraction.</p>
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<p>The different filters used in the filtered back-projection method [<a href="#B32-jimaging-09-00112" class="html-bibr">32</a>].</p>
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<p>Measurements obtained for different metal rods.</p>
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<p>Slice reconstructed for each rod.</p>
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<p>Diameter reconstructed in pixels according to the measured one.</p>
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17 pages, 10707 KiB  
Article
Lithium Metal Battery Quality Control via Transformer–CNN Segmentation
by Jerome Quenum, Iryna V. Zenyuk and Daniela Ushizima
J. Imaging 2023, 9(6), 111; https://doi.org/10.3390/jimaging9060111 - 31 May 2023
Cited by 1 | Viewed by 2092
Abstract
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to [...] Read more.
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning: Trends and Applications)
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<p>Diagram illustrating the Li–polymer–Li symmetric cell design, imaged using X-ray CT, with highlighted dendrite formations (blue) and the redeposited Li (red).</p>
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<p>Cross- sectional images for the Li–polymer–Li symmetric cell; (<b>a</b>) cross section of the x–y plane where the training was completed on this plane; (<b>b</b>) cross sections of the x–z plane and detailing of the cell components; (<b>c</b>) cross sections of the y–z plane.</p>
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<p>Schematic illustration of the pouch cell.</p>
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<p>Sample raw data obtained after TomoPy reconstruction of a CT scan.</p>
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<p>Sample region of interest (RoI) data obtained after preprocessing TomoPy reconstruction of a CT scan.</p>
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<p>XCT cross–sections and corresponding segmentation results for U-Net, Y-Net, T-Net (TransforCNN) with inputs in 1st column characterized by: (<b>a</b>) high concentration of large Li agglomerates, (<b>b</b>) moderate concentration of small Li agglomerates, (<b>c</b>) low concentration of small Li agglomerates, (<b>d</b>) low concentration of large Li agglomerates, (<b>e</b>–<b>g</b>) low concentration of small Li agglomerates that were not labeled by humans but detected by the deep learning algorithms, (<b>h</b>) low concentration of small Li agglomerates.</p>
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<p>U-Net architecture.</p>
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<p>Y-Net architecture.</p>
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<p>TransforCNN architecture combines the encoder of Vision Transformers with the CNN decoder.</p>
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<p>Training curves for U-Net, Y-Net, and TransforCNN on the <span class="html-italic">y</span>-axis vs. numbers of epochs on the <span class="html-italic">x</span>-axis; the models were optimized over the binary cross-entropy function as loss and evaluated on the Dice similarity coefficient as evaluation metric during training. The gray curves depict behavior on the validation sets while the black curves show behavior on the training sets over increasing numbers of epochs; (<b>a</b>) training and validation Dice coefficient for U-Net; (<b>b</b>) training and validation dice coefficient for Y-Net; (<b>c</b>) training and validation Dice coefficient for TransforCNN; (<b>d</b>) training and validation loss for U-Net; (<b>e</b>) training and validation loss for Y-Net; (<b>f</b>) training and validation loss for U-Net; the use of dropout during only the training phase explains why the models tend to perform better on the validation set over time.</p>
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<p>XCT cross-section along the x–y plane and corresponding segmentation results for U-Net, Y-Net, T-Net (TransforCNN), and E-Net.</p>
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<p>3D rendering of U-Net, Y-Net, T-Net (TransforCNN), and E-Net on test volume; (<b>a</b>) grayscale test input volume; (<b>b</b>–<b>e</b>) are, respectively, U-Net, Y-Net, T-Net (TransforCNN), and E-Net predictions.</p>
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12 pages, 797 KiB  
Article
A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
by Fatima Zahra Benabdallah, Ahmed Drissi El Maliani, Dounia Lotfi and Mohammed El Hassouni
J. Imaging 2023, 9(6), 110; https://doi.org/10.3390/jimaging9060110 - 31 May 2023
Cited by 5 | Viewed by 2094
Abstract
Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put [...] Read more.
Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%. Full article
(This article belongs to the Topic Medical Image Analysis)
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<p>The 3D matrices construction process. MST and MaxST represent the minimum spanning tree and the maximum spanning tree, respectively.</p>
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<p>Steps to extract the Spanning Trees matrices from a connectivity matrix.</p>
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<p>The main steps of deep learning classification.</p>
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<p>Classification before and after tuning.</p>
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11 pages, 2015 KiB  
Article
Gender, Smoking History, and Age Prediction from Laryngeal Images
by Tianxiao Zhang, Andrés M. Bur, Shannon Kraft, Hannah Kavookjian, Bryan Renslo, Xiangyu Chen, Bo Luo and Guanghui Wang
J. Imaging 2023, 9(6), 109; https://doi.org/10.3390/jimaging9060109 - 29 May 2023
Cited by 5 | Viewed by 1923
Abstract
Flexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. The diagnostic performance can be improved when patients’ [...] Read more.
Flexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. The diagnostic performance can be improved when patients’ demographic information is incorporated into models. However, the manual entry of patient data is time-consuming for clinicians. In this study, we made the first endeavor to employ deep learning models to predict patient demographic information to improve the detector model’s performance. The overall accuracy for gender, smoking history, and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a new laryngoscopic image set for the machine learning study and benchmarked the performance of eight classical deep learning models based on CNNs and Transformers. The results can be integrated into current learning models to improve their performance by incorporating the patient’s demographic information. Full article
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<p>Illustration of using deep learning models for laryngeal image classification. The deep learning models were pretrained on ImageNet and then fine-tuned on the laryngeal dataset using transfer learning. The output prediction could be gender, smoking history, or age.</p>
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<p>The average loss curve for training the models to predict age, gender, and smoking history. The left, middle, and right graphs are the loss curves for predicting age, gender, and smoking history, respectively.</p>
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<p>The CAM visualization of gender prediction. “pred” stands for the predicted result, and “gt” represents the ground truth. The left column demonstrates the maps for male patients, and the right column illustrates the maps for female patients. The red color indicates the areas in the image that have a high response for the predicted result, and the blue color means the areas ion the image that have a low response for the predicted result.</p>
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<p>The CAM visualization of smoking history prediction. “pred” stands for the predicted result, and “gt” represents the ground truth. The left column demonstrates the maps for male patients, and the right column illustrates the maps for female patients. The red color indicates the areas in the image have a high response for the predicted result, and the blue color means the areas in the image have a low response for the predicted result.</p>
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<p>The CAM visualization of age prediction. “pred” stands for the predicted result, and “gt” represents the ground truth. The left column demonstrates the maps for male patients, and the right column illustrates the maps for female patients. The red color indicates the areas in the image that have a high response for the predicted result, and the blue color means the areas in the image that have a low response for the predicted result.</p>
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14 pages, 3162 KiB  
Article
Transformative Effect of COVID-19 Pandemic on Magnetic Resonance Imaging Services in One Tertiary Cardiovascular Center
by Tatiana A. Shelkovnikova, Aleksandra S. Maksimova, Nadezhda I. Ryumshina, Olga V. Mochula, Valery K. Vaizov, Wladimir Y. Ussov and Nina D. Anfinogenova
J. Imaging 2023, 9(6), 108; https://doi.org/10.3390/jimaging9060108 - 28 May 2023
Cited by 3 | Viewed by 2012
Abstract
The aim of study was to investigate the transformative effect of the COVID-19 pandemic on magnetic resonance imaging (MRI) services in one tertiary cardiovascular center. The retrospective observational cohort study analyzed data of MRI studies (n = 8137) performed from 1 January [...] Read more.
The aim of study was to investigate the transformative effect of the COVID-19 pandemic on magnetic resonance imaging (MRI) services in one tertiary cardiovascular center. The retrospective observational cohort study analyzed data of MRI studies (n = 8137) performed from 1 January 2019 to 1 June 2022. A total of 987 patients underwent contrast-enhanced cardiac MRI (CE-CMR). Referrals, clinical characteristics, diagnosis, gender, age, past COVID-19, MRI study protocols, and MRI data were analyzed. The annual absolute numbers and rates of CE-CMR procedures in our center significantly increased from 2019 to 2022 (p-value < 0.05). The increasing temporal trends were observed in hypertrophic cardiomyopathy (HCMP) and myocardial fibrosis (p-value < 0.05). The CE-CMR findings of myocarditis, acute myocardial infarction, ischemic cardiomyopathy, HCMP, postinfarction cardiosclerosis, and focal myocardial fibrosis prevailed in men compared with the corresponding values in women during the pandemic (p-value < 0.05). The frequency of myocardial fibrosis occurrence increased from ~67% in 2019 to ~84% in 2022 (p-value < 0.05). The COVID-19 pandemic increased the need for MRI and CE-CMR. Patients with a history of COVID-19 had persistent and newly occurring symptoms of myocardial damage, suggesting chronic cardiac involvement consistent with long COVID-19 requiring continuous follow-up. Full article
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<p>Percentage of CE-CMR studies among all MRI studies in one tertiary cardiovascular center year-by-year. * <span class="html-italic">p</span>-value &lt; 0.05, CE-CMR: contrast-enhanced cardiac MRI.</p>
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<p>Distribution of cardiac pathologies in male and female patients who underwent CE-CMR studies in one tertiary cardiovascular center during COVID-19 pandemic. AMI—acute myocardial infarction, EF—ejection fraction, ICMP—ischemic cardiomyopathy, HCMP—hypertrophic cardiomyopathy, PICS—postinfarction cardiosclerosis. Horizontal axis corresponds to the number of patients. * <span class="html-italic">p</span>-value &lt; 0.05 for sex-related difference. <sup>§</sup> <span class="html-italic">p</span>-value &lt; 0.05 for significant difference in the incidence compared with the pre-pandemic year (2019). <sup>#</sup> <span class="html-italic">p</span>-value &lt; 0.05 for significant difference in the incidence compared with the previous year (2021 vs. 2020).</p>
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<p>Age distribution of patients who received CE-CMR studies in one tertiary cardiovascular center during COVID-19 pandemic. Vertical axis corresponds to the number of patients. Horizontal axis corresponds to the age groups. <sup>§</sup> <span class="html-italic">p</span>-value &lt; 0.05 for significant difference in absolute number compared with the pre-pandemic year (2019). <sup>#</sup> <span class="html-italic">p</span>-value &lt; 0.05 for significant difference in the incidence compared with the previous year. * <span class="html-italic">p</span>-value &lt; 0.05 for the highest increment in the number compared with other age groups.</p>
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<p>Distribution of cardiovascular diseases in patients who received CE-CMR studies in one tertiary cardiovascular center during COVID-19 pandemic. <sup>§</sup> <span class="html-italic">p</span>-value &lt; 0.05 compared with the incidence in the pre-pandemic year (2019). <sup>#</sup> <span class="html-italic">p</span>-value &lt; 0.05 compared with the incidence in the previous year (2021 vs. 2020).</p>
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<p>Contrast-enhanced cardiac magnetic resonance imaging in a patient with history of COVID-19 and three Lake Louise criteria of myocardial inflammation: edema, hyperemia, and fibrosis. White arrows indicate (<b>a</b>) edema (T2-weighted image), (<b>b</b>) hyperemia of the left ventricular inferior wall (early post-contrast enhancement on T1-weighted image), and (<b>c</b>) fibrosis (late contrast enhancement in inversion recovery mode).</p>
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<p>Pericardial effusion, cardiac chamber dilatation, and decline in the left ventricular (VL) contractility in the group of patients with fibrotic dystrophy changes. Darker sectors correspond to the proportion of patients who had these changes.</p>
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<p>Trends in diagnostic imaging procedures during COVID-19 pandemic. (<b>A</b>) Number of confirmed COVID-19 cases in the region month-by-month. (<b>B</b>) Number of MRI studies performed during COVID-19 pandemic in one tertiary cardiovascular center. (<b>C</b>) Number of CE-CMR studies. (<b>D</b>) Percentage of CE-CMR studies among all MRI studies. Red lines indicate the corresponding linear trends.</p>
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34 pages, 5370 KiB  
Article
A Siamese Transformer Network for Zero-Shot Ancient Coin Classification
by Zhongliang Guo, Ognjen Arandjelović, David Reid, Yaxiong Lei and Jochen Büttner
J. Imaging 2023, 9(6), 107; https://doi.org/10.3390/jimaging9060107 - 25 May 2023
Cited by 4 | Viewed by 2502 | Correction
Abstract
Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of [...] Read more.
Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin’s design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method’s errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking. Full article
(This article belongs to the Special Issue Pattern Recognition Systems for Cultural Heritage)
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<p>Examples of two different specimens of the same issue, namely of RIC 439 <span class="html-italic">Aelius</span> denarius.</p>
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<p>The architecture of a Siamese neural network comprises two mutually mirroring processing streams consisting of two identical neural networks with shared hyperparameters [<a href="#B25-jimaging-09-00107" class="html-bibr">25</a>].</p>
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<p>The architecture of Single Siamese ViT.</p>
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<p>The architecture of Double Siamese ViT.</p>
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<p>The data distribution of the train set.</p>
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<p>The flow chart for organizing data set.</p>
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<p>Performance characteristics of the proposed Single Siamese ViT on the obverse matching task.</p>
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<p>Performance characteristics of the proposed Single Siamese ViT on the reverse matching task.</p>
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<p>Training behavior of our Double Siamese ViT.</p>
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<p>Summary of matching accuracy shown averaged over each issuing authority shown on the obverse.</p>
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<p>Examples of RIC 158 of <span class="html-italic">Augustus</span>. (<b>a</b>) An incomplete specimen of RIC 158 of <span class="html-italic">Augustus</span>. (<b>b</b>) A good condition specimen of RIC 158 of <span class="html-italic">Augustus</span>.</p>
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<p>Examples of defective and complete specimens of <span class="html-italic">Augustus</span> in our data set. (<b>a</b>) An incomplete specimen of RIC 160 of <span class="html-italic">Augustus</span>. (<b>b</b>) A complete specimen of RIC 160 of <span class="html-italic">Augustus</span>.</p>
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<p>Examples of worn and discolored coins.</p>
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<p>Examples of non-conforming data entries: (<b>a</b>) two specimens of Mariniana, also unusually shown reverse first then obverse, and (<b>b</b>) four diverse specimens, incorrectly matched as a whole with the issue corresponding to the specimen on the top left.</p>
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<p>An example of two different issues which are virtually identical in their semantic content.</p>
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21 pages, 23878 KiB  
Article
Manipulating Pixels in Computer Graphics by Converting Raster Elements to Vector Shapes as a Function of Hue
by Tajana Koren Ivančević, Nikolina Stanić Loknar, Maja Rudolf and Diana Bratić
J. Imaging 2023, 9(6), 106; https://doi.org/10.3390/jimaging9060106 - 23 May 2023
Viewed by 1619
Abstract
This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape [...] Read more.
This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape is done depending on the detected color values for each pixel. The CMYK values are first converted to the corresponding RGB values and then to the HSB system, and the vector shape is selected based on the obtained hue values. The vector shape is drawn in the defined space, according to the row and column matrix of the pixels of the original CMYK image. Twenty-one vector shapes are introduced to replace the pixels depending on the hue. The pixels of each hue are replaced by a different shape. The application of this conversion has its greatest value in the creation of security graphics for printed documents and the individualization of digital artwork by creating structured patterns based on the hue. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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<p>Translation of the original image in jpeg to resulting image with shapes defined by color hue.</p>
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<p>Shows 12 pixels of different hues and their replacement with a new fish shape.</p>
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<p>(<b>a</b>) Shows four colors where the original pixels are replaced by four new shapes, and (<b>b</b>) shows enlarged details of the pixel layout.</p>
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<p>Four basic colors of the CMYK system and three basic colors of the RGB system in which the new shapes replace the original pixel shape.</p>
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<p>Shows the pixels replaced by vector shapes for 11 hues by the HSB system and 1 for black.</p>
Full article ">Figure 6
<p>(<b>a</b>) Showing the color wheel divided into 20 hues and (<b>b</b>) the result obtained by replacing the pixels with vector shapes.</p>
Full article ">Figure 7
<p>Shows enlarged details of <a href="#jimaging-09-00106-f006" class="html-fig">Figure 6</a>b. Some hues responded correctly to the program’s requirements, others did not. Some shapes appear in more than one hue interval (green waves, blue lines).</p>
Full article ">Figure 8
<p>Showing a color wheel with 21 different shapes (20 + black) replacing the original pixels with corrected intervals of hue values.</p>
Full article ">Figure 9
<p>(<b>a</b>) Original image of colorful bouquet of flowers (Source: <a href="https://i.pinimg.com/originals/92/a5/3f/92a53f294d9309d526e2469765028a75.jpg" target="_blank">https://i.pinimg.com/originals/92/a5/3f/92a53f294d9309d526e2469765028a75.jpg</a>, visited on 29 April 2023) and (<b>b</b>) the resulting image after the pixels were replaced by different shapes based on the detected hue values.</p>
Full article ">Figure 10
<p>The enlarged details of <a href="#jimaging-09-00106-f009" class="html-fig">Figure 9</a>.</p>
Full article ">Figure 11
<p>The image of Easter eggs. (<b>a</b>) Shows the original image and (<b>b</b>) shows the result after applying our method.</p>
Full article ">Figure 12
<p>Shows the enlarged details of <a href="#jimaging-09-00106-f011" class="html-fig">Figure 11</a>.</p>
Full article ">Figure 13
<p>Proposed method implemented on pop art image of Marilyn Monroe (Source: <a href="https://www.pxfuel.com/en/desktop-wallpaper-eejre" target="_blank">https://www.pxfuel.com/en/desktop-wallpaper-eejre</a>, visited on 2 May 2023), creating numerous different solutions based on the change of shapes for the same hue.</p>
Full article ">Figure 14
<p>(<b>a</b>) Right distribution of the shapes obtained with our method. (<b>b</b>) The distribution of the shapes obtained using Uncoated Fogra 29 color setting—deviations are visible in the letters S, C, I, Y, P, A, S, S.</p>
Full article ">Figure 15
<p>(<b>a</b>) Shows the original security graphic and (<b>b</b>) shows the result after printing and rescanning the graphic.</p>
Full article ">Figure 16
<p>Enlarged details of <a href="#jimaging-09-00106-f015" class="html-fig">Figure 15</a>; (<b>a</b>) original security graphics and (<b>b</b>) enlarged details of rescanned graphics.</p>
Full article ">Chart 1
<p>Shows the values from <a href="#jimaging-09-00106-t001" class="html-table">Table 1</a> for the first four columns, i.e., the correlation of C, M, Y as a function of hue, the mathematically determined values (<b>large image</b>), and the values obtained by measurement for different color settings (<b>5 small images</b>).</p>
Full article ">Chart 2
<p>The comparison of the distribution of hue values obtained mathematically by dividing the circle into 20 equal parts (delta) and hue intervals for correctly displayed shape distributions. <a href="#jimaging-09-00106-t002" class="html-table">Table 2</a> shows the values obtained mathematically at the beginning, with intervals of 18 degrees and the values for the correct representation of the shape by the individual segments.</p>
Full article ">Chart 3
<p>The difference between the mathematically determined hue intervals (inner circle data—intervals of 18 degrees) and those after correction showing the result of our method (outer circle data—different hue intervals). <a href="#jimaging-09-00106-ch004" class="html-fig">Chart 4</a> shows the difference between the initial and final values of the hue interval determined by the program and those measured in the case of a regular shape distribution on the color wheel. Since the differences between the initial and final values of the hue interval in the program solution are always 18, straight lines (blue and orange) are obtained, while oscillations (gray and yellow) can be seen in the corrected values.</p>
Full article ">Chart 4
<p>Shows the difference between the initial and final values of the hue interval determined mathematically by the program, and those measured in the case of a regular shape distribution on the color wheel.</p>
Full article ">
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