An expert vision system for medical image segmentation

Shiuh Yung Chen, Wei Chung Lin, Chin Tu Chen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this paper, an expert vision system is proposed which integrates knowledge from diverse sources for tomographic image segmentation. The system miinicks the reasoning process of an expert to divide a tomographic brain image into semantically meaningful entities. These entities can then be related to the fundamental biomedical processes, both in health and in disease, that are of interest or of importance to health care research. The images under study include those acquired from x-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and PET (Positron Emission Tomography). Given a set of three (correlated) images acquired from these three different modalities at the same slicing level and angle of a human brain, the proposed system performs image segmentation based on (1) knowledge about the characteristics of the three different sensors, (2) knowledge about the anatomic structures of human brains, (3) knowledge about brain diseases, and (4) knowledge about image processing and analysis tools. Since the problem domain is characterized by incomplete and uncertain information, the blackboard architecture which is an opportunistic reasoning model is adopted as the framework of the proposed system.

Original languageEnglish (US)
Pages (from-to)162-172
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - May 25 1989

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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