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Proceedings Paper

Neural networks for medical image segmentation
Author(s): Wei-Chung Lin; Chen-Kuo Tsao; Chin-Tu Chen; Yu-Jen Feng
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Paper Abstract

A class of Constraint Satisfaction Neural Networks (CSNN) is proposed for solving the problem of medical image segmentation which can be formulated as a Constraint Satisfaction Problem (CSP). A CSNN consists of a set of objects, a set of labels for each object, a collection of constraint relations linking the labels of neighboring objects, and a topological constraint describing the neighborhood relationships among various objects. Each label for a particular object indicates one possible interpretation for that object. The CSNN can be viewed as a collection of neurons that interconnect with each other. The connections and the topology of a CSNN are used to represent the constraints in a CSP. The mechanism of the neural network is to find a solution that satisfies all the constraints in order to achieve a global consistency. The final solution outlines segmented areas and simultaneously satisfies all the constraints. This technique has been applied to many images in different domains and the results show that this CSNN method is a very promising approach for image segmentation.

Paper Details

Date Published: 1 June 1991
PDF: 10 pages
Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); doi: 10.1117/12.45235
Show Author Affiliations
Wei-Chung Lin, Northwestern Univ. (United States)
Chen-Kuo Tsao, Northwestern Univ. (United States)
Chin-Tu Chen, Univ. of Chicago (United States)
Yu-Jen Feng, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 1445:
Medical Imaging V: Image Processing
Murray H. Loew, Editor(s)

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