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Proceedings Paper

Knowledge-based classification and tissue labeling of magnetic resonance images of human brain
Author(s): ChunLin Li; Lawrence O. Hall; Dmitry B. Goldgof
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Paper Abstract

This paper presents a knowledge based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of human brain. The system consists of two components: an unsupervised clustering algorithm and an expert system. MR brain data is initially segmented by the unsupervised algorithm, then, the expert system locates a focus-of- attention tissue or cluster and analyzes it by matching it with a model or searching in it for an expected feature. The focus-of-attention tissue location and its analysis are repeated until a tumor is found or all tissues are labeled. Abnormal slices are labeled by reclustering regions of interest with knowledge accumulated from previous analysis. The domain knowledge contains tissue distribution in feature space acquired with a clustering algorithm, and tissue models. Default reasoning is used to match a qualitative model with its instances. The system has been tested with fifty-three slices acquired at different times by two different scanners.

Paper Details

Date Published: 29 July 1993
PDF: 12 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148667
Show Author Affiliations
ChunLin Li, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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