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Proceedings Paper

Three-dimensional image segmentation using neural networks
Author(s): Jin-Shin Chou; Chin-Tu Chen; Wei-Chung Lin
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Paper Abstract

We have integrated a neural network model. Kohonen's self-organizing feature maps, with the idea of fuzzy sets and applied this model to the problem of 3-D image segmentation. In the proposed method, a Kohonen network provides the basic structure and update rule, whereas fuzzy membership values control the learning rate. The calculation of learning rate is based on a fuzzy clustering algorithm. The experimental results show that the speed of convergence is fast. The major strength of the proposed approach is its unsupervised nature. Moreover, the computer memory requirement is smaller and the computation time is less than that of a conventional 3-D region-based method.

Paper Details

Date Published: 29 July 1993
PDF: 8 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148669
Show Author Affiliations
Jin-Shin Chou, Univ. of Chicago and Northwestern Univ. (United States)
Chin-Tu Chen, Univ. of Chicago (United States)
Wei-Chung Lin, Northwestern Univ. (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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