A variational framework for multiregion pairwise-similarity-based image segmentation

Variational cost functions that are based on pairwise similarity between pixels can be minimized within the level set framework, resulting in a binary image segmentation. In this paper, we extend such cost functions and address the multiregion image segmentation problem by employing a multiphase level set framework. For multimodal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work [1], proposed for background-foreground separation, to the segmentation of images into more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley Segmentation Data Set by comparing its performance with other segmentation techniques.