Color image segmentation for liver tissue images

Yung Nien Sun, Chung Sheng Wu, Zl Zhang Lin, Nan Hwa Chou

Research output: Contribution to journalArticle

Abstract

An automatic tissue characterization system is always in great demands by pathologists. However, the existing systems are either too time consuming or impractical to analyze liver tissue images. In this paper, we have developed a highly parallel and effective system based on color image segmentation to analyze liver tissue images. To simplify the complex tissue segmentation problem, we first utilize the achromatic information (the intensity) to coarsely segment tissue image, then make use of the chromatic information to classify segmented tissues into different tissue classes. Thus, this automatic system includes an unsupervised probabilistic relaxation classification process and a supervised Bayes classification process. Because the invariant properties of liver tissue image are fully utilized, the computational complexity is greatly reduced. Experimental studies also show reliable results in liver tissue classification for clinical applications. Index Terms, Color image, segmentation, classification, probabilistic relaxation, Bayes classification, voting logic, rank filter.

Original languageEnglish
Pages (from-to)174-182
Number of pages9
JournalBiomedical Engineering - Applications, Basis and Communications
Volume5
Issue number2
Publication statusPublished - 1993 Jan 1

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Bioengineering
  • Biomedical Engineering

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