Statistical region snake-based segmentation adapted to different physical noise models

Algorithms for object segmentation are crucial in many image processing app lications. During past years, active contour models (snakes) have been wide ly used for finding the contours of objects. This segmentation strategy is classically edge-based in the sense that the snake is driven to fit the max imum of an edge map of the scene. In this paper, we propose a region snake approach and we determine fast algorithms for the segmentation of an object in an image. The algorithms developed in a Maximum Likelihood approach are based on the calculation of the statistics of the inner and the outer regi ons (defined by the snake). It has thus been possible to develop optimal al gorithms adapted to the random fields which describe the gray levels in the input image if we assume that their probability density function family ar e known. We demonstrate that this approach is still efficient when no bound ary's edge exists in the image. We also show that one can obtain fast algor ithms by transforming the summations over a region, for the calculation of the statistics, into summations along the boundary of the region. Finally, we will provide numerical simulation results for different physical situati ons in order to illustrate the efficiency of this approach.