Variational Bayesian level set for image segmentation
Hanbing Qu, Lin Xiang, Jiaqiang Wang, Bin Li, Hai-Jun Tao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
In this paper, we present a variational Bayesian framework for level set image segmentation, which utilizes Gaussian mixtures model to approximate the posteriors of image intensities inside and outside of the zero level set, respectively. The active curve will evolve according to the approximate log marginal probability of each region and a partition of image is obtained by the sign of the level set function. Our method provides a flexible probabilistic framework to model image data with flexible Gaussian mixtures model. Experimental results demonstrate our approach is comparable to classical level set segmentation method.