Multilabel statistical shape prior for image segmentation
Damien Grosgeorge, Caroline Petitjean, Su Ruan · IET Image Processing · 2016
Statistical shape models have been widely used to guide the segmentation in an image, thus overcoming noise and occlusions. In this study, the authors present a graph cut‐based segmentation framework, in which multiple objects can be segmented. They design a specific multilabel shape prior, which is integrated into the graph cost function. They also want to enforce spatial constraint between the objects. Towards this aim, they propose a local constraint to forbid the inclusion of an object into another, which is enforced in the regularisation term of the graph energy. They apply the authors’ method to cardiac magnetic resonance images, in which left and right ventricles, and the myocardium are segmented and for which encouraging results are obtained.