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9 May 2002 MR-image-based tissue analysis and its clinical applications
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This paper presents a three-dimensional (3-D) tissue analysis method and its applications in partial volume correction and change analysis. The method uses a stochastic model-based approach and consists of two steps: (1) unsupervised tissue quantification and (2) 3-D segmentation. Firstly, the MR image volume is modeled by the standard finite normal mixture (SFNM) distribution. It has been shown that the SFNM converges to the true distribution when the pixel images are asymptotically independent. Secondly, the tissue quantification is achieved through (1) model selection by minimum description length (MDL) criterion; (2) parameter initialization by optimal histogram quantization and (3) parameter estimation by a fast EM algorithm using the global 3-D histogram rather than conventionally the raw data. Finally, we develop a 3-D segmentation method using the maximum likelihood (ML) classification and contextual Bayesian relaxation labeling (CBRL). The CBRL is developed to obtain a consistent labeling solution, based on localized SFNM formulation by using neighborhood contextual regularities. The method has been applied to partial volume correction for PET brain images and change analysis for MR breast images.
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Kun Huang, Jianhua Xuan, Jozsef Varga, Matthew T. Freedman M.D., Zsolt Szabo, Dan Hayes, Vered Stearns, and Yue Joseph Wang "MR-image-based tissue analysis and its clinical applications", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002);

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