Medical Image Segmentation by a Constraint Satisfaction Neural Network

Chin Tu Chen, Eric Chen Kuo Tsao, Wei Chung Lin

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

A class of Constraint Satisfaction Neural Networks (CSNNs) 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 relationship 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 medical images and the results show that this CSNN method is a very promising approach for image segmentation.

Original languageEnglish (US)
Pages (from-to)678-686
Number of pages9
JournalIEEE Transactions on Nuclear Science
Volume38
Issue number2
DOIs
StatePublished - Apr 1991

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering

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