Statistical approach to segmentation of single-channel cerebral MR images
- 1 April 1997
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 16 (2) , 176-186
- https://doi.org/10.1109/42.563663
Abstract
A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.Keywords
This publication has 78 references indexed in Scilit:
- Parameter estimation and tissue segmentation from multispectral MR imagesIEEE Transactions on Medical Imaging, 1994
- Unsupervised tissue type segmentation of 3D dual-echo MR head dataImage and Vision Computing, 1992
- Partial volume tissue classification of multichannel magnetic resonance images-a mixel modelIEEE Transactions on Medical Imaging, 1991
- Unsupervised texture segmentation using Markov random field modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- A statistical method for automatic labeling of tissues in medical imagesMachine Vision and Applications, 1990
- Random field models in image analysisJournal of Applied Statistics, 1989
- Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random FieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1987
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Image segmentation using simple Markov field modelsComputer Graphics and Image Processing, 1982
- The use of Markov Random Fields as models of textureComputer Graphics and Image Processing, 1980