Efficient Statistical Shape Models-Based Image Segmentation Approach Using Deformable Simplex Meshes

Principal component analysis (PCA) based statistical shape models (SSMs) are widely employed to incorporate high-level a priori shape knowledge of the structure to be segmented to achieve robustness. In this paper, a novel mesh-to-volume registration based shape correspondence establishment method was proposed to improve the accuracy and reduce the computational cost. Specifically, we present a greedy algorithm based deformable simplex mesh that uses vector field convolution (VFC) as the external energy. Furthermore, we develop an automatic shape initialization method by using a Gaussian mixture model (GMM) based registration algorithm, to derive an initial shape that has high overlap with the object of interest, such that the deformable models can then evolve more locally. We apply the proposed deformable surface model to the application of femur statistical shape model construction to illustrate its accuracy and efficiency. The experimental results suggest that our method can be employed for effective statistical shape model construction.