Shape prior regularized continuous max-flow approach to image segmentation

In this work, we propose a novel segmentation method based on the continuous max-flow (CMF) formulation of the Potts model incorporating the statistical shape model. We increase the robustness and accuracy of the Potts model by using the prior shape knowledge from the Principal Component Analysis (PCA) to represent the desired shape. Our multi-label model can segment several objects simultaneously and guarantee one label with the structure similar to the shape prior. The proposed approach is applied to both synthetic and medical image of liver from computed tomography (CT). Numerous numerical experiments demonstrate that our model is efficient and with good quality in practice.