Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network

Jzau Sheng Lin, Kuo Sheng Cheng, Chi Wu Mao

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)


This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for 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 Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

Original languageEnglish
Pages (from-to)205-214
Number of pages10
JournalInternational Journal of Bio-Medical Computing
Issue number3
Publication statusPublished - 1996 Aug

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)


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