Color image analysis for liver tissue images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An automatic tissue characterization system is always in great demand by pathologists. However, the existing methods are either too simple to classify a complicated liver tissue image or dependent on heavy human intervention and very time consuming. 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 tissue classification problem, the system first utilizes the achromatic information (the intensity) to coarsely segment the tissue image, then makes use of the chromatic information to classify the segmented regions into four different tissue classes. Thus, the proposed method includes an unsupervised probabilistic relaxation segmentation process and a supervised Bayes classification process. Because the invariant grey level and color properties of the liver tissue image are fully utilized, the difficult classification problem can be well fulfilled at a reasonable computational cost. The proposed method also shows reliable liver tissue classification results from different test sample sets.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsMurray H. Loew
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Pages453-464
Number of pages12
ISBN (Print)0819411310
Publication statusPublished - 1993 Dec 1
EventMedical Imaging 1993: Image Processing - Newport Beach, CA, USA
Duration: 1992 Feb 141992 Feb 19

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1898
ISSN (Print)0277-786X

Other

OtherMedical Imaging 1993: Image Processing
CityNewport Beach, CA, USA
Period92-02-1492-02-19

All Science Journal Classification (ASJC) codes

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

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