CRF-driven Implicit Deformable Model

We present a topology independent solution for segmenting objects with texture patterns of any scale, using an implicit deformable model driven by conditional random fields (CRFs). Our model integrates region and edge information as image driven terms, whereas the probabilistic shape and internal (smoothness) terms use representations similar to the level-set based methods. The evolution of the model is solved as a MAP estimation problem, where the target conditional probability is decomposed into the internal term and the image-driven term. For the later, we use discriminative CRFs in two scales, pixel- and patch-based, to obtain smooth probability fields based on the corresponding image features. The advantages and novelties of our approach are (i) the integration of CRFs with implicit deformable models in a tightly coupled scheme, (ii) the use of CRFs which avoids ambiguities in the probability fields, (iii) the handling of local feature variations by updating the model interior statistics and processing at different spatial scales, and (v) the independence from the topology. We demonstrate the performance of our method in a wide variety of images, from the zebra and cheetah examples to the left and right ventricles in cardiac images.