Articles

Cellular neural network architecture for Gibbs random-field-based image segmentation

[+] Author Affiliations
Chang Wen Chen

University of Missouri-Columbia, Department of Electrical Engineering, Columbia, Missouri 65211

Lulin Chen

University of Rochester, Department of Electrical Engineering, Rochester, New York 14627

J. Electron. Imaging. 7(1), 45-51 (Jan 01, 1998). doi:10.1117/1.482625
History: Received Mar. 5, 1997; Revised July 10, 1997; Accepted Aug. 5, 1997
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Abstract

We describe a novel cellular connectionist neural network model for the implementation of clustering-based Bayesian image segmentation with Gibbs random-field spatial constraints. The success of this algorithm is largely due to the neighborhood constraints modeled by the Gibbs random field. However, the iterative enforcement of the neighborhood constraints involved in the Bayesian estimation would generally require tremendous computational power. Such computational requirement hinders the real-time application of the Bayesian image segmentation algorithms. The cellular connectionist model proposed aims at implementing the Bayesian image segmentation with real-time processing potentials. With a cellular neural network architecture mapped onto the image spatial domain, the powerful Gibbs spatial constraints are realized through the interactions among neurons connected through their spatial cellular layout. This network model is structurally similar to the conventional cellular network. However, in this new cellular model, the processing elements designed within the connectionist network are functionally more versatile to meet the challenging needs of Bayesian image segmentation based on the Gibbs random field. We prove that this cellular neural network does converge to the desired steady state with a properly designed update scheme. Examples of CT volumetric medical image segmentation are presented to demonstrate the potential of this cellular neural network for a specific image segmentation application. © 1998 SPIE and IS&T.

© 1998 SPIE and IS&T

Citation

Chang Wen Chen and Lulin Chen
"Cellular neural network architecture for Gibbs random-field-based image segmentation", J. Electron. Imaging. 7(1), 45-51 (Jan 01, 1998). ; http://dx.doi.org/10.1117/1.482625


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