Segmentation of multispectral MR images through an annealed rough neural network

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/6887

Title:	Segmentation of multispectral MR images through an annealed rough neural network
Authors:	林灶生
Contributors:	資訊工程系
Date:	2011-08
Issue Date:	2017-12-17 13:46:52 (UTC+8)
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.
Relation:	Neural Comput & Applic
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

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