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A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Images
April 1992 (vol. 4 no. 2)
pp. 156-161

The gray-white decision network is introduced as an application of a recurrent cooperative/competitive network for segmentation of magnetic resonance (MR) brain images. The three-layer dynamical system relaxes into a solution where each pixel is labeled as either gray matter, white matter, or other matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a neurally inspired recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Applications of the network and its phase plane analysis are presented.

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Index Terms:
neural nets; magnetic resonance brain images; gray-white decision network; recurrent cooperative/competitive network; three-layer dynamical system; pixel; gray matter; white matter; raw input intensity; edge information; neighbor interactions; phase plane analysis; biomedical NMR; brain; computerised pattern recognition; computerised picture processing; medical computing; neural nets
Citation:
A.J. Worth, S. Lehar, D.N. Kennedy, "A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Images," IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 156-161, April 1992, doi:10.1109/69.134252
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