Abstract

Recently, new segmentation models based on local informa- tion have emerged. They combine local statistics of the regions along the contour (inside and outside) to drive the segmentation procedure. Since they are based on local decisions, these models are more robust to lo- cal variations of the regions of interest (contrast, noise, blur, . . . ). They nonetheless also introduce some new difficulties which are inherent to the fact of basing a global property (the segmentation) on pure local deci- sions. This papers explores some of those difficulties and proposes some possible corrections. Results on both 2D and 3D data are compared to those obtained without these corrections.