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Image Segmentation Based on Improved Otsu Algorithm


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Image Segmentation Based on Improved Otsu Algorithm
LI Min1LUO Hong-yan1ZHENG Xiao-lin1TAN Li-wen2ZHU Wen-wu1
1.Bioengineering College,Chongqing University,Chongqing 400030,China; 2.Department of Anatomy,Third Military Medical University,Chongqing 400038,China
maximum between-duster variance(Otsu) image segmentation threshhold white matters open operation
According to the features of white matter in the human brain slice images,an improved maximum between-cluster variance(Otsu)algorithm for the image segmentation is proposed by introducing the average variance into the conventional Otsu method.The images are pre-segmented by the connected component labeling method and then filtered by means of the open operation of morphology.Finally,the improved Otsu theory is applied to extract the white matter from the slice images.The proposed algorithm is compared with the conventional Otsu algorithm in terms of their performances on the segmentation of white matter in sequential slice images qualitatively,and evaluated quantitatively by using the missed detection rate and the false detection rate.The average missed detection rates of the two methods are all 0.03 around,and the average false declection are 0.026 and 0.251 respectively.The results indicate that the improved Otsu method characterized by the combination of the region information and edge information can lead to more accurate and effective segmentation.


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Last Update: 2012-10-12