Mr image-based tissue analysis and its clinical applications

Kun Huang, Jianhua Xuan, Jozsef Varga, Matthew Freedman, Zsolt Szabo, Dan Hayes, Vered Stearns, Yue Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)424-432
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4684 I
StatePublished - 2002


  • Change analysis
  • Fastem
  • Partial volume correction
  • Segmentation
  • T issue analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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