Integration of Gibbs Prior models and deformable models for 3D medical image segmentation

Proposes a methodology for 3D medical image segmentation based on the integration of 3D deformable and Markov random field models. Our method makes use of Markov random field theory to build Gibbs Prior models for the 3D medical image with arbitrary initial parameters to estimate the organ boundary. Then we use a 3D deformable model to fit the estimated boundary under the influence of gradient information in the initial 3D image and the balloon force. The result of the deformable model fit is used to update the Gibbs Prior model parameters, such as the gradient threshold of a boundary. Based on the updated parameters we restart the Gibbs Prior models. By integrating these processes recursively we achieve an automated segmentation of the initial 3D images. Our segmentation solution greatly reduces the time for 3D segmentation process and is capable of getting out of local minim. Results of the method are presented for several examples, including some MRI images with significant amount of noise.