Probabilistic shape-based segmentation method using level sets

In this study, a novel probabilistic, geometric and dynamic shape-based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two-dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges.