Tissue probability map constrained 4D clustering algorithm for increased accuracy and robustness in SerialMR brain image segmentation

Zhong Xue, Dinggang Shen, Hai Li, Stephen T.C. Wong

Research output: Contribution to journalArticle

Abstract

The traditional fuzzy clustering algorithms might be biased due to the variability of tissue intensities and anatomical structures: clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we propose a tissue probability map constrained clustering algorithm and apply it to series of 3D MR brain images of the same subject at different time points for longitudinal study. The tissue probability maps consist of segmentation priors obtained from a population and reflect the probability of different tissue types. Using the proposed algorithm in the framework of the CLASSIC algorithm, which iteratively segments a series of images and estimates their longitudinal deformations, more accurate and robust longitudinal measures can be achieved. Experimental results using both simulated and real data confirmed the advantages of the proposed algorithm in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders.

Original languageEnglish
Pages (from-to)286-298
Number of pages13
JournalInternational Journal of Medical Engineering and Informatics
Volume3
Issue number3
DOIs
Publication statusPublished - 2011 Oct

Keywords

  • Image segmentation
  • Longitudinal image processing
  • MR brain images

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biomaterials
  • Biomedical Engineering
  • Health Informatics

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