Modified Hopfield neural network with fuzzy c-means technique for multispectral MR image segmentation

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

Research output: Contribution to conferencePaper

10 Citations (Scopus)

Abstract

This paper presents a modified Hopfield neural network with fuzzy c-means technique for segmenting multispectral MR brain images. The proposed approach is a novel unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique, and is suitable for parallel implementation in the application of medical image segmentation. The fuzzy c-means clustering strategy is included in the Hopfield neural network so as to eliminate the need of the 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. From the experimental results, it is shown that a near optimal solution can be obtained using the proposed method.

Original languageEnglish
Pages327-330
Number of pages4
Publication statusPublished - 1996 Dec 1
EventProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz
Duration: 1996 Sep 161996 Sep 19

Other

OtherProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
CityLausanne, Switz
Period96-09-1696-09-19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Lin, J. S., Cheng, K-S., & Mao, C. W. (1996). Modified Hopfield neural network with fuzzy c-means technique for multispectral MR image segmentation. 327-330. Paper presented at Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3), Lausanne, Switz, .