Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns †
<p>Reference ultrasound images [<a href="#B7-jimaging-04-00003" class="html-bibr">7</a>] used in our work.</p> "> Figure 2
<p>Artist rendered synthetic image and its cropped version using a sector region.</p> "> Figure 3
<p>The simulation and evaluation stages of the processing pipeline.</p> "> Figure 4
<p>Sampling models that can be used in simulating speckle noise ([<a href="#B13-jimaging-04-00003" class="html-bibr">13</a>], reproduced with permission).</p> "> Figure 5
<p>Effect of changing axial resolution (<span class="html-italic">m</span>) in radial-polar sampling ([<a href="#B13-jimaging-04-00003" class="html-bibr">13</a>], reproduced with permission).</p> "> Figure 6
<p>Effect of changing axial resolution (<span class="html-italic">m</span>) in radial-uniform sampling ([<a href="#B13-jimaging-04-00003" class="html-bibr">13</a>], reproduced with permission).</p> "> Figure 7
<p>Image artifacts produced by large values of sampling and noise parameters ([<a href="#B13-jimaging-04-00003" class="html-bibr">13</a>], reproduced with permission).</p> "> Figure 8
<p>Application of the proposed local binary patterns (LBP) features in the evaluation of filtering algorithms.</p> "> Figure 9
<p>The intermediate steps in the computation of the LBP histogram of an image.</p> "> Figure 10
<p>(<b>a</b>) A synthetic ultrasound image; (<b>b</b>) The LBP image; (<b>c</b>) the LBP histogram.</p> "> Figure 11
<p>Synthetic images generated using radial polar sampling with a coarse to fine variation of lateral resolution parameter <span class="html-italic">n</span>.</p> "> Figure 12
<p>Variations of LBP feature vector components with lateral resolution in radial-polar sampling. The <span class="html-italic">x</span>-axis gives the values of <span class="html-italic">n</span>. The <span class="html-italic">y</span>-axis gives the range of values of an LBP feature shown in the chart title.</p> "> Figure 13
<p>Synthetic images generated using radial uniform sampling with a coarse to fine variation of lateral resolution parameter <span class="html-italic">n<sub>u</sub></span>.</p> "> Figure 14
<p>Variations of LBP feature vector components with lateral resolution in radial-uniform sampling. The <span class="html-italic">x</span>-axis gives the values of <span class="html-italic">n<sub>u</sub></span>. The <span class="html-italic">y</span>-axis gives the range of values of an LBP feature shown in the chart title.</p> "> Figure 15
<p>Synthetic images generated using uniform-grid sampling scheme with increasing values of the grid spacing parameter <span class="html-italic">δ</span>.</p> "> Figure 16
<p>Variations of LBP feature vector components with grid spacing in uniform-grid sampling. The <span class="html-italic">x</span>-axis gives the values of <span class="html-italic">δ</span>. The <span class="html-italic">y</span>-axis gives the range of values of an LBP feature shown in the chart title.</p> "> Figure 17
<p>Plots showing the closest matching positions of the LBP feature vector with reference vector for images generated using (<b>a</b>) radial-polar sampling; (<b>b</b>) radial-uniform sampling; (<b>c</b>) uniform-grid sampling.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
3. The Modeling and Speckle Simulation
x(i, j) = dj cos θi + w/2; y(i, j) = −dj sin θi; i = 0…(n − 1); j = 0…(m − 1)
f(x, y, θ) = (x − w/2) sinθ + y cosθ
4. Synthetic Ultrasound Images
5. Analysis of Texture Features
5.1. Local Binary Patterns (LBP)
5.2. LBP Features of Synthetic Ultrasound Images
6. Experimental Analysis and Validation
6.1. LBP Feature Vector for Reference Images
- When the parameters controlling the resolution in a sampling method are adjusted from coarse to fine, do the values of the corresponding LBP feature vector consistently tend towards the reference feature vector?
- Do the synthetic images that give feature values close to the reference vector also have consistently high subjective evaluation scores assigned by clinical experts?
- Which one of the three modelling schemes generated feature values that are closest to the reference feature vector?
6.2. LBP Feature Vector for Radial-Polar Sampling
6.3. LBP Feature Vector for Radial-Uniform Sampling
6.4. LBP Feature Vector for Uniform-Grid Sampling
6.5. Comparative Analysis of Sampling Techniques
7. Conclusions and Future Work
- Radial polar: when the parameter n is increased from 10 to 110
- Radial uniform: when the parameter nu is increased from 10 to 100
- Uniform grid: when the spacing parameter δ is reduced from 14 to 2
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Singh, P.; Mukundan, R.; De Ryke, R. Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns. J. Imaging 2018, 4, 3. https://doi.org/10.3390/jimaging4010003
Singh P, Mukundan R, De Ryke R. Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns. Journal of Imaging. 2018; 4(1):3. https://doi.org/10.3390/jimaging4010003
Chicago/Turabian StyleSingh, Prerna, Ramakrishnan Mukundan, and Rex De Ryke. 2018. "Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns" Journal of Imaging 4, no. 1: 3. https://doi.org/10.3390/jimaging4010003