Region-Based Segmentation via Non-Rigid Template Matching

We propose a new region segmentation method based on non-rigid template matching. We align a binary template to an image by maximizing the likelihood of intensity distributions within a region of interest and its background. The intensity model and the corresponding a posteriori distributions are estimated and updated throughout the alignment. The geometric deformation of the template is based on a fluid registration model. Unlike contour-based segmentation techniques, this registration framework allows for a global regularization of the template variations. This enables the segmentation of irregular shapes while avoiding leaks. We apply our method to the segmentation of the liver in computed tomography images, a challenging task due to the high inter-patient variability in the shape of this organ. We show that our segmentation results are equivalent or superior in accuracy to results obtained using existing techniques based on 3D shape models.