Using object probabilities in deformable model fitting

We present a novel image segmentation method based on statistical shape model fitting. Instead of fitting the model to raw intensity values we consider object probabilities. The abstraction from the plain intensity images to probability maps makes the segmentation more robust against misleading texture inside the object or surrounding background. The target object probability is predicted based on random forest regression trained with neighborhood dependent features of sample images. In contrast to similar approaches, both, the object boundary as well as the whole object and background region are considered for segmentation. We apply our approach to a 3D cone beam computed tomography image dataset of the jaw region where we segment the wisdom tooth shape. Compared to a boundary and a region-based method we obtain superior segmentation performance.