Effective fuzzy clustering techniques for segmentation of breast MRI |
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Authors: | S R Kannan A Sathya S Ramathilagam |
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Affiliation: | (1) Department of Mathematics, Gandhihgram Rural University, Gandhigram, 624302, Tamil Nadu, India;(2) Present address: Department of Electrical Engineering, National Cheng Kung University (NCKU), University Road, Tainan, 70101, Taiwan;(3) Department of Engineering Science, Room No. 41126, National Cheng Kung University (NCKU), University Road, Tainan, 70101, Taiwan |
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Abstract: | The goal of this work is to segment the breast into different regions, each corresponding to a different tissue, and to identify
tissue regions judged abnormal, based on the signal enhancement-time information. There are a number of problems that render
this task complex. Breast MRI segmentation based on the differential enhancement of image intensities can assist the clinician
to detect suspicious regions. In this paper, we propose an effective segmentation method for breast contrast-enhanced MRI
(ce-MRI). The segmentation method is developed based on standard fuzzy clustering techniques proposed by Bezedek. By minimizing
the proposed effective objective function, this paper obtains an effective way of predicting membership grades for objects
and new method to update centers. Experiments will be done with a synthetic image to show how effectively the new proposed
effective fuzzy c-means (FCM) works in obtaining clusters. To show the performance of proposed FCM, this work compares the
results with results of standard FCM algorithm on same synthetic image. Then the proposed method was applied to segment the
clinical ce-MR images with the help of computer programing language and results have been shown visually. |
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