Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleDecember 2023
Supervised Domain Adaptation by transferring both the parameter set and its gradient
AbstractA well-known obstacle in the successful implementation of deep learning-based systems to real-world problems is the performance degradation that occurs when applying a network that was trained on data collected in one domain, to data from a ...
Highlights- We present supervised domain adaptation methods that transfer both the parameter set and its derivative.
- In MMTL the adaptation procedure is based on using examples from both the source and target domains.
- DMMTL is a variant of ...
- ArticleMay 2023
Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization
Scale Space and Variational Methods in Computer VisionPages 511–521https://doi.org/10.1007/978-3-031-31975-4_39AbstractThe segmentation of MRI is a challenging task due to artifacts introduced by the acquisition process, like bias field and noise. In this paper, using a cartoon-texture decomposition of the image, we present a strategy that segments the cartoon ...
- ArticleSeptember 2022
Unsupervised Site Adaptation by Intra-site Variability Alignment
AbstractA medical imaging network that was trained on a particular source domain usually suffers significant performance degradation when transferred to a different target domain. This is known as the domain-shift problem. In this study, we propose a ...
- ArticleSeptember 2022
Supervised Domain Adaptation Using Gradients Transfer for Improved Medical Image Analysis
AbstractA well known problem in medical imaging is the performance degradation that occurs when using a model learned on source data, in a new site. Supervised Domain Adaptation (SDA) strategies that focus on this challenge, assume the availability of a ...
-
- research-articleSeptember 2022
Deep pattern-based tumor segmentation in brain MRIs
Neural Computing and Applications (NCAA), Volume 34, Issue 17Pages 14317–14326https://doi.org/10.1007/s00521-022-07422-yAbstractIt is still hard to deal with artifacts in magnetic resonance images (MRIs), particularly when the latter are to be segmented. This paper introduces a novel deep-based scheme for tumor segmentation in brain MRIs. According to the proposed scheme, ...
- ArticleAugust 2022
TBC-Unet: U-net with Three-Branch Convolution for Gliomas MRI Segmentation
Intelligent Computing Theories and ApplicationPages 53–65https://doi.org/10.1007/978-3-031-13829-4_5AbstractSegmentation networks with encoder and decoder structures provide remarkable results in the segmentation of gliomas MRI. However, the network loses small-scale tumor feature information during the encoding phase due to the limitations of the ...
- ArticleMarch 2022
Fully Automatic Brain Tumor Segmentation by Using Competitive EM and Graph Cut
AbstractManual MRI brain tumor segmentation is a difficult and time consuming task which makes computer support highly desirable. This paper presents a hybrid brain tumor segmentation strategy characterized by the allied use of Graph Cut segmentation ...
- research-articleFebruary 2021
Gaussian-kernel c-means clustering algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 25, Issue 3Pages 1699–1716https://doi.org/10.1007/s00500-020-04924-6AbstractPartitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for ...
- ArticleOctober 2020
A Generalizable Deep-Learning Approach for Cardiac Magnetic Resonance Image Segmentation Using Image Augmentation and Attention U-Net
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC ChallengesPages 287–296https://doi.org/10.1007/978-3-030-68107-4_29AbstractCardiac cine magnetic resonance imaging (CMRI) is the reference standard for assessing cardiac structure as well as function. However, CMRI data presents large variations among different centers, vendors, and patients with various cardiovascular ...
- ArticleOctober 2019
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification ChallengesPages 300–308https://doi.org/10.1007/978-3-030-39074-7_32AbstractLeft ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural ...
- research-articleMarch 2019
A transform-based fast fuzzy C-means approach for high brain MRI segmentation accuracy
Applied Soft Computing (APSC), Volume 76, Issue CPages 156–173https://doi.org/10.1016/j.asoc.2018.12.005AbstractSegmentation of brain magnetic resonance (MR) images has a significant impact on the computer-aided diagnosis and analysis. However, due to the presence of noise in medical images, many segmentation methods suffer from limited ...
Highlights- A fast DCT-based MR image segmentation approach proposed.
- The approach provides ...
- research-articleJuly 2018
A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images
Applied Soft Computing (APSC), Volume 68, Issue CPages 447–457https://doi.org/10.1016/j.asoc.2018.03.054Highlights- A transform-based local and nonlocal fuzzy C-means (DCT-LNLFCM) for brain MRI segmentation is developed.
Accurate segmentation of brain tissues from magnetic resonance images (MRI) is a crucial requirement for the quantitative analysis of brain images. Due to the presence of noise in brain MRI, many segmentation methods suffer from low ...
- articleNovember 2017
A Metaheuristically Tuned Interval Type 2 Fuzzy System to Reduce Segmentation Uncertainty in Brain MRI Images
Journal of Medical Systems (JMSY), Volume 41, Issue 11Pages 1–14https://doi.org/10.1007/s10916-017-0821-5Precise segmentation of magnetic resonance image (MRI) seems challenging because of the complex structure of the brain, non-uniform field in images, and noise. As a result, decision-making is associated with uncertainty. Fuzzy based approaches have been ...
- research-articleSeptember 2017
MR images-Based Microwave Focusing for Thermal Therapy
RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent SystemsPages 126–131https://doi.org/10.1145/3129676.3129728A microwave (MW) focusing technique for non-invasive thermal therapy is presented. The proposed technique provides a thermal focusing at localized tissues as a cancer tissue and a deep tissue for muscular disorder treatment. We employed a time reversal ...
- ArticleSeptember 2015
Entropy-Based Automatic Segmentation and Extraction of Tumors from Brain MRI Images
CAIP 2015: Proceedings, Part II, of the 16th International Conference on Computer Analysis of Images and Patterns - Volume 9257Pages 195–206https://doi.org/10.1007/978-3-319-23117-4_17We present a method for automatic segmentation and tumor extraction for brain MRI images. The method does not require preliminary training, and uses an extended concept of image entropy. The latter is computed over gray levels which are in fixed number ...
- articleOctober 2013
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease
Pattern Recognition Letters (PTRL), Volume 34, Issue 14Pages 1725–1733https://doi.org/10.1016/j.patrec.2013.04.014This paper presents a novel computer-aided diagnosis (CAD) tool for the diagnosis of the Alzheimer's disease (AD) using structural Magnetic Resonance Images (MRIs). The proposed method uses information learnt from the tissue distribution of Gray Matter (...
- articleJune 2013
Parameterization of the distribution of white and grey matter in MRI using the α-stable distribution
Computers in Biology and Medicine (CBIM), Volume 43, Issue 5Pages 559–567https://doi.org/10.1016/j.compbiomed.2013.01.003This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of @a-stable distributions. A Bayesian @a-stable ...
- articleSeptember 2012
A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data
Pattern Recognition (PATT), Volume 45, Issue 9Pages 3463–3471https://doi.org/10.1016/j.patcog.2012.03.009The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An ...
- ArticleJuly 2012
Comparison of hologic's quantra volumetric assessment to MRI breast density
IWDM'12: Proceedings of the 11th international conference on Breast ImagingPages 619–626https://doi.org/10.1007/978-3-642-31271-7_80Interest in measuring breast tissue density due to its association with breast cancer risk grows, though the majority of studies use qualitative density measures manually reported by radiologists, which are time-consuming and costly. The purpose of this ...