Neural networks for medical image segmentation

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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


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). 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.

Original languageEnglish (US)
Pages (from-to)376-385
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Jan 1 1991
EventMedical Imaging V: Image Processing - San Jose, CA, USA
Duration: Feb 27 1991Mar 1 1991

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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