Active Contours: A New Distribution Metric for Image Segmentation
How can we characterize an image, and does the same characterization yield the same result? In this work, we study one possible characterization, distribution metrics. That is, we assume the desired region of interest has a different probability distribution from its corresponding background. Using this, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies “distance” for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation.
For More Information: www.romeilsandhu.com/research_projects/p
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Romeil Sandhu (2024). Active Contours: A New Distribution Metric for Image Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/26101-active-contours-a-new-distribution-metric-for-image-segmentation), MATLAB Central File Exchange. Retrieved .
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- Mathematics and Optimization > Mapping Toolbox > Geometric Geodesy >
- Radar > Mapping Toolbox > Geometric Geodesy >
- Sciences > Biological and Health Sciences > Biomedical Imaging >
- Image Processing and Computer Vision > Image Processing Toolbox > Image Segmentation and Analysis > Image Segmentation > Active contours >
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