Effective fuzzy clustering techniques for segmentation of breast MRI

  • Authors:

  • S. R. Kannan;A. Sathya;S. Ramathilagam

  • Affiliations:

  • Gandhihgram Rural University, Department of Mathematics, 624302, Gandhigram, Tamil Nadu, India and National Cheng Kung University (NCKU), Department of Electrical Engineering, University Road, 701 ...;Gandhihgram Rural University, Department of Mathematics, 624302, Gandhigram, Tamil Nadu, India;National Cheng Kung University (NCKU), Department of Engineering Science, Room No. 41126, University Road, 70101, Tainan, Taiwan

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Digital Information Forensics
  • Year:
  • 2011

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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.