Interactive 2D and volume image segmentation using level sets of probabilities

Abstract

In this technical sketch, we adopt the level set method for image segmentation that integrates region statistics and edge responses. It is well-known that a serious limitation of existing level set algorithms for image segmentation is that the final result is sensitive to the location of the initialization. This is because level set evolution is typically driven by forces computed from local image data. We overcome this problem by adopting a novel level set function based on foreground probabilities, and further integrating the level set method with a probabilistic pixel classifier [Liu and Yu 2012]. Since an accurate classifier does not exist at the beginning, the segmentation framework is based on the expectation-maximization (EM) algorithm. In summary, the motivations for our method based on level sets of probabilities are manifold.