Fuzzy Hopfield neural network with fixed weight for medical image       segmentation

Chwen-Liang Chang; Yu-Tai Ching

doi:10.1117/1.1428298

1 February 2002 Fuzzy Hopfield neural network with fixed weight for medical image segmentation

Chwen-Liang Chang, Yu-Tai Ching

Optical Engineering, Vol. 41, Issue 2, (February 2002). https://doi.org/10.1117/1.1428298

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.

©(2002) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Chwen-Liang Chang and Yu-Tai Ching "Fuzzy Hopfield neural network with fixed weight for medical image segmentation," Optical Engineering 41(2), (1 February 2002). https://doi.org/10.1117/1.1428298

Published: 1 February 2002

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