A novel variational method for liver segmentation based on statistical shape model prior and enforced local statistical feature

Medical image segmentation plays an important role in digital medical research, therapy planning, and computer aided diagnosis. However, the existence of noise and low contrast make automatic liver segmentation remains an open challenge. In this work we focus on a novel variational semi-automatic liver segmentation method. First, we used the signed distance functions (SDF) representing pattern shapes to build statistical shape model. Then global Gaussian fitting energy and enforced local feature fitting energy were established to guide the PCA-based topological transformation. We used the unconstrained shape coefficients and geometric transformation parameters to make the proposed method robust in a wide variety of pathological cases. Experiments on two public available datasets demonstrated that the proposed liver segmentation method achieves competitive results to that of the state-of-the-art.