Gibbs prior models, marching cubes, and deformable models: A hybrid framework for 3D medical image segmentation

Abstract

Hybrid frameworks combining region-based and boundary-based segmentation methods have been used in 3D medical image segmentation applications. In this paper we propose a hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. We use Gibbs models to create 3D binary masks of the object. Then we use the marching cubes method to initialize a deformable model based on the mask. The deformable model will fit to the object surface driven by the gradient information in the original image. The deformation result will then be used to update the parameters of Gibbs models. These methods will work recursively to achieve a final segmentation. By using the marching cubes method, we succeed in improving the accurancy and efficiency of 3D segmentation. We validate our method by comparing the segmentation result with expert manual segmentation, the results show that high quality segmentation can be achieved with computational efficiency.