Model-Based Multi-Object Segmentation via Distribution Matching

Daniel Freedman, Richard J. Radke, Tao Zhang, Yongwon Jeong, George T. Y. Chen

Abstract: A new algorithm for the segmentation of objects from 3D images using deformable models is presented. This algorithm relies on learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image; instead, probability distributions are compared. This allows for a faster, more principled algorithm. Furthermore, the algorithm is not sensitive to the form of the shape model, making it quite flexible. Results of the algorithm are shown for the segmentation of the prostate and bladder from medical images.