Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation

We incorporate gradient information and probability scores from a classifier to construct ESF.

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:
1. http://pratondo.staff.telkomuniversity.ac.id/2016/01/14/robust-edge-stop-functions-for-edge-based-active-contour-models-in-medical-image-segmentation/
2 http://ieeexplore.ieee.org/document/7353157/
3. http://www.sciencedirect.com/science/article/pii/S1047320316302486