Document Detail

Brain tissue classification of magnetic resonance images using partial volume modeling.
MedLine Citation:
PMID:  11212366     Owner:  NLM     Status:  MEDLINE    
This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk alpha of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.
S Ruan; C Jaggi; J Xue; J Fadili; D Bloyet
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on medical imaging     Volume:  19     ISSN:  0278-0062     ISO Abbreviation:  IEEE Trans Med Imaging     Publication Date:  2000 Dec 
Date Detail:
Created Date:  2001-02-09     Completed Date:  2001-05-03     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  8310780     Medline TA:  IEEE Trans Med Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1179-87     Citation Subset:  IM    
Greyc-Ismra, Cnrs Umr 6072, Caen, France.
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MeSH Terms
Brain / anatomy & histology*
Magnetic Resonance Imaging*
Markov Chains
Models, Statistical
Normal Distribution

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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