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Segmentation of multispectral MR images through an annealed rough neural network

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Abstract

In this paper, multispectral image segmentation using a rough neural network based on an annealed strategy with a cooling schedule is created. The main purpose is to embed an annealed cooling schedule into the rough neural network to construct a segmentation system named annealed rough neural net (ARNN). The classification system is a paradigm for the implementation of annealed reasoning and rough systems in neural network architecture. Instead of all the information in the image are fed into the neural network, the upper- and lower-bound gray level, captured from a training vector in a multispectral image, were fed into a rough neuron in the ARNN. Therefore, only 2-channel images are selected as the training samples if an N-dimensional multispectral image was used. In the simulation results, the proposed network not only reduces the consuming time but also reserves the classification performance.

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Acknowledgments

This work was supported by the National Science Council, TAIWAN, under the Grants NSC98-2221-E-167-016-MY2.

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Authors and Affiliations

  1. Electrical Engineering Department of National Cheng Kung University, No. 1, University Road, Tainan City, 701, Taiwan, ROC

    Yi-Ying Chang & Shen-Chuan Tai

  2. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, No. 35, Lane 215, Sec. 1, Chung-Shan Rd., Taiping, Taichung, Taiwan, ROC

    Jzau-Sheng Lin

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  1. Yi-Ying Chang
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  2. Shen-Chuan Tai
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  3. Jzau-Sheng Lin
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Correspondence to Yi-Ying Chang.

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Chang, YY., Tai, SC. & Lin, JS. Segmentation of multispectral MR images through an annealed rough neural network. Neural Comput & Applic 21, 911–919 (2012). https://doi.org/10.1007/s00521-011-0724-1

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