Hybrid variational image segmentation techniques, involving energy functionals 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 functionals 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.