Greedy algorithm based deformable simplex meshes using gradient vector flow as external energy
Changfa Shi, Changyong Guo, Yuanzhi Cheng, Jinke Wang · 2014
Deformable models have been quite popular in medical image analysis, particularly in image segmentation. However, when applied to 3D volumetric data, their high computational cost can be a problem. In this paper, we describe a new efficient 3D segmentation method based on deformable simplex meshes. The greedy algorithm, which has proven more computational efficient and robust than physics-based method, is employed to perform the shape deformation. Generalized gradient vector flow (GGVF) field is a classical external force for physics-based deformable models. We adapt it for greedy algorithm as external energy to overcome the main issues of the traditional external energy (i.e., sensitivity to shape initialization and poor convergence to the long and thin boundary concavities). Results of applying our method to both synthetic and clinical images are presented to illustrate the accuracy and robustness of our proposed method.