Globally Segmentation Using Active Contours and Belief Function

We study the active contours (AC) based globally segmentation for vector-valued image incorporating both statistical and evidential knowledge. The proposed method combine both Belief Functions (BFs) and probability functions in the same framework. In this formulation, all features issued from vector-valued image are integrated in inside/outside descriptors to drive the segmentation process based AC. In this formulation, the imprecision caused by the weak contrast and noise between inside and outside descriptors issued from the multiple channels is controlled by the BFs as weighted parameters. We demonstrated the performance of our segmentation algorithm using some challenging color biomedical images.