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150 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 5, NO. 2, JUNE 2001 Magnetic Resonance Image Analysis by Information
 

Summary: 150 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 5, NO. 2, JUNE 2001
Magnetic Resonance Image Analysis by Information
Theoretic Criteria and Stochastic Site Models
Yue Wang, Member, IEEE, Tülay Adali, Member, IEEE, Jianhua Xuan, and Zsolt Szabo
Abstract--Quantitative analysis of magnetic resonance (MR)
images is a powerful tool for image-guided diagnosis, monitoring,
and intervention. The major tasks involve tissue quantification and
image segmentation where both the pixel and context images are
considered. To extract clinically useful information from images
that might be lacking in prior knowledge, we introduce an unsu-
pervised tissue characterization algorithm that is both statistically
principled and patient specific. The method uses adaptive standard
finite normal mixture and inhomogeneous Markov random field
models, whose parameters are estimated using expectation-max-
imization and relaxation labeling algorithms under information
theoretic criteria. We demonstrate the successful applications of
the approach with synthetic data sets and then with real MR brain
images.
Index Terms--Finite normal mixture, image segmentation, in-
formation theoretic criteria, patient site model, tissue quantifica-

  

Source: Adali, Tulay - Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County

 

Collections: Engineering