Neural networks for medical image segmentation

Wei-Chung Lin; Chen-Kuo Tsao; Chin-Tu Chen; Yu-Jen Feng

doi:10.1117/12.45235

1 June 1991 Neural networks for medical image segmentation

Wei-Chung Lin, Chen-Kuo Tsao, Chin-Tu Chen, Yu-Jen Feng

Proceedings Volume 1445, Medical Imaging V: Image Processing; (1991) https://doi.org/10.1117/12.45235
Event: Medical Imaging '91, 1991, San Jose, CA, United States

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.

Citation Download Citation

Wei-Chung Lin, Chen-Kuo Tsao, Chin-Tu Chen, and Yu-Jen Feng "Neural networks for medical image segmentation", Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); https://doi.org/10.1117/12.45235

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