Medical volume segmentation based on level sets of probabilities

Abstract

In this paper, we present a robust and accurate method for biomedical image segmentation using level sets of probabilities. The level set method is a popular technique in biomedical image segmentation. Our method integrates a probabilistic classifier with the level set method, making the level set method less vulnerable to local minima. Given the local attributes within a neighborhood of a voxel, this classifier outputs an estimated likelihood of the voxel being part of an object of interest. Our method obtains a posterior probabilistic mask of the object of interest according to such estimated likelihoods, an edge field and a smoothness prior. We further alternate classifier training and the level set method to improve the performance of both. We have successfully applied our method to the segmentation of various organs and tissues in the Visible Human dataset. Experiments and comparisons demonstrate our method can accurately extract volumetric objects of interest, and outperforms traditional levelset-based segmentation algorithms.