Share Email Print

Optical Engineering

Fuzzy Hopfield neural network with fixed weight for medical image segmentation
Author(s): Chwen-Liang Chang; Yu-Tai Ching
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Image segmentation is a process for dividing a given image into meaningful regions with homogeneous properties. A new two step approach is proposed for medical image segmentation using a fuzzy Hopfield neural network based on both global and local gray-level information. The membership function simulated with neuron outputs is determined using a fuzzy set, and the synaptic connection weights between the neurons are predetermined and fixed to improve the efficiency of the neural network. The proposed method needs initial cluster centers. The initial centers can be obtained from the global information about the distribution of the intensities in the image, or from prior knowledge of the intensity of the region of interest. It is shown by experiments that the proposed fuzzy Hopfield neural network approach is better than most previous approaches. We also show that the global information can be used by applying the hard c-means to estimate the initial cluster centers.

Paper Details

Date Published: 1 February 2002
PDF: 8 pages
Opt. Eng. 41(2) doi: 10.1117/1.1428298
Published in: Optical Engineering Volume 41, Issue 2
Show Author Affiliations
Chwen-Liang Chang, National Chiao Tung Univ. (Taiwan)
Yu-Tai Ching, National Chiao Tung Univ. (Taiwan)

© SPIE. Terms of Use
Back to Top