Segmentation of multispectral MR images through an annealed rough neural network

Yi Ying Chang, Shen Chuan Tai, Jzau Sheng Lin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)911-919
Number of pages9
JournalNeural Computing and Applications
Issue number5
Publication statusPublished - 2012 Jul

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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