An approach to adaptive stereo brain image's segmentation.
- Author:
Yong-Hong SHI
1
;
Fei-Hu QI
Author Information
1. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030. shi-yh@cs.sjtu.edu.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artificial Intelligence;
Brain;
anatomy & histology;
Humans;
Image Enhancement;
methods;
Image Processing, Computer-Assisted;
methods;
Information Storage and Retrieval;
methods;
Magnetic Resonance Imaging;
methods;
Markov Chains;
Models, Statistical
- From:
Chinese Journal of Medical Instrumentation
2006;30(2):88-87
- CountryChina
- Language:Chinese
-
Abstract:
This paper presents a new method for automatically segmenting brain parenchyma and cerebrospinal fluid in routine single-echo MR images. This method is based on the coupled Markov models. They can model intensity measurement at each voxel site to implement piecewise smoothness constraint, and at the same time, model discontinuities to control the interaction between each pair of the neighboring voxel. The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields (MRFs) models. This method has the following desirable properties: (1) the brain image can be well classified into white matter, grey matter and cerebrospinal fluid (CSF), and (2) it has a better robustness to noise and intensity inhomogeneity.
