Region-based statistical segmentation using informational active contours

Hybrid variational image segmentation techniques, involving energy functional which combine contour- and region-based terms, have been actively investigated due to their ability to jointly integrate shape and texture cues about scene objects. Minimizing these functional can be efficiently achieved using curve evolution techniques, yielding region competition models along the deforming segmentation boundaries. Within this framework, this paper presents a novel region-based statistical active contour approach to segmentation, refered to as info-snakes. Here, the segmentation problem is expressed as the maximization of an information-theoretic similarity measure between the image luminance distribution, and the label distribution of a regional template defining a multi-object geometric prior model, subject to regularization constraints on region boundaries. The probability densities associated with luminance distributions within each template region are estimated using a nonparametric Parzen technique, which avoids resorting to prior assumptions on image statistics or to a training phase. We shall focus our attention on the Ali-Silvey class of information measures, and derive the corresponding gradient flows over nonparametric smooth curve spaces. As expected, the evolution equations for the template boundaries interpret as a statistical region competition model, promoting statistically consistent regions relative to the chosen information metrics. An efficient implementation using a multiphase level set technique is finally provided. Experiments on a cardiac perfusion MRI dataset are presented, demonstrating the relevance of info-snakes for implementing computer-assisted diagnosis tools in cardiology.