Distribution-Based Active Contour Model for Medical Image Segmentation

Having being regarded as one of the classical methods in image segmentation, geodesic active contours (GAC) have the flaws of boundary leaking and expensive evolving time. In this paper, we present a distribution-based active contour model by measuring the Bhattacharyya distance between probability distributions of the object and background along with the evolution of GAC model. Due to combining the image cues of edge and statistical information which is computed by using kernel density estimation, this hybrid methodology prevents the boundary leaking as well as the under segmentation problem. Experimental results on the medical images show the improvements of our method in terms of comparisons with original GAC model, Bhattacharyya gradient flow, texture-based GAC and Li's active contour model.