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- W2386324334 abstract "In the research of medical image segmentation,it is difficult to determine the number of segmentation classes.To solve the problem,a novel measurement for determining the number of classes named mean divergence function was formed according to the relation among three common means.And then an image segmentation method based on mean divergence and simulated annealing was proposed.In this method,the mean divergence function is used as an optimization object and simulated annealing is used as an optimization method to find the optimal segmentation threshold in overall search space.This overcomes the shortcomings of fuzzy C-means(FCM) clustering algorithm,such as it is hard to determine the number of classes and easy to get into a local extremum.Experimental results show that this method could automatically segment the medical image with focus,and the speed had significant improvement compared with the method based on mutual information." @default.
- W2386324334 created "2016-06-24" @default.
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- W2386324334 date "2010-01-01" @default.
- W2386324334 modified "2023-09-25" @default.
- W2386324334 title "An image segmentation method based on mean divergence" @default.
- W2386324334 hasPublicationYear "2010" @default.
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