a fuzzy hopfield neural network for medical image segmentation

Jzau-Sheng Lin Jzau-Sheng, Kuo Sheng Cheng, Chi Wu Mao

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to class centers. In order to generate feasible results, a fuzzy c-means clustering strategy is included in the Hopfleld neural network to eliminate the need of finding weighting factors in the energy function, which is formulated and based on a basic concept commonly used in pattern classification, called the within-class scatter matrix principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfleld neural network. The fuzzy Hopfleld neural network based on the withinclass scatter matrix shows the promising results in comparison with the hard c-means method.

Original languageEnglish
Pages (from-to)2389-2398
Number of pages10
JournalIEEE Transactions on Nuclear Science
Issue number4 PART 2
Publication statusPublished - 1996

All Science Journal Classification (ASJC) codes

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


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