Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbors and the support vector machine confirm the effectiveness of the proposed approach.
Please also see the following pages:
gus (2022). Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/62709-robust-edge-stop-functions-for-edge-based-active-contour-models-in-medical-image-segmentation), MATLAB Central File Exchange. Retrieved .
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