Robust Edge Stop Functions for Edge Based Active Contour Models in Medical Image Segmentation
Edge based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. 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 neighbours and the support vector machine confirm the effectiveness of the proposed approach.
IEEE Signal Processing Letters