TY - JOUR
T1 - Segmentation of multispectral MR images through an annealed rough neural network
AU - Chang, Yi Ying
AU - Tai, Shen Chuan
AU - Lin, Jzau Sheng
N1 - Funding Information:
This work was supported by the National Science Council, TAIWAN, under the Grants NSC98-2221-E-167-016-MY2.
PY - 2012/7
Y1 - 2012/7
N2 - 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.
AB - 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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U2 - 10.1007/s00521-011-0724-1
DO - 10.1007/s00521-011-0724-1
M3 - Article
AN - SCOPUS:84865616390
VL - 21
SP - 911
EP - 919
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 5
ER -