We present a novel method to effectively segment the three dimensional MR brain images (volumes) with severe intensity nonuniformity. The segmentation problem was formulated using maximum a posterior probability and Markov random filed (MAP-MRF) framework. A novel spatial Gaussian mixture model (SGMM) is used to represent the intensity probability distribution of each of the three brain tissues (WM, GM and CSF), and MRF is used to compute the prior probability. This method consists of a learning process based on expectation maximization algorithm (EM) to estimate the parameters of SGMM, and a classification algorithm based on iterated conditional modes (ICM) to perform the segmentation of the sequential brain images using the parameters obtained from the learning process. The results on the simulated and twenty in vivo MR brain volumes demonstrate the efficiency of this method. We also present the comparison results with other published methods.
MR brain imaging segmentation based on spatial Gaussian mixture model and Markov random field
2005-01-01
188408 byte
Conference paper
Electronic Resource
English
Mr Brain Imaging Segmentation Based on Spatial Gaussian Mixture Model and Markov Random Field
British Library Conference Proceedings | 2005
|Gaussian Markov random field based improved texture descriptor for image segmentation
British Library Online Contents | 2014
|Segmentation of Rumex obtusifolius using Gaussian Markov random fields
British Library Online Contents | 2013
|Video Object Segmentation Based on Gaussian Mixture Model
British Library Online Contents | 2006
|Driving intention recognition method based on Gaussian mixture-hidden Markov model
European Patent Office | 2024
|