Improved Fuzzy Clustering Algorithms in Segmentation of DC-enhanced breast MRI

Authors:
S. R. Kannan;S. Ramathilagam;Pandiyarajan Devi;A. Sathya
Affiliations:
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan 70701 and Department of Mathematics, Gandhigram Rural University, Gandhigram, India 624 302;Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan 70701;Department of Mathematics, Gandhigram Rural University, Gandhigram, India 624 302;Department of Mathematics, Gandhigram Rural University, Gandhigram, India 624 302
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

Segmentation of medical images is a difficult and challenging problem due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. The objective of this work is to develop some robust fuzzy clustering segmentation systems for effective segmentation of DCE - breast MRI. This paper obtains the robust fuzzy clustering algorithms by incorporating kernel methods, penalty terms, tolerance of the neighborhood attraction, additional entropy term and fuzzy parameters. The initial centers are obtained using initialization algorithm to reduce the computation complexity and running time of proposed algorithms. Experimental works on breast images show that the proposed algorithms are effective to improve the similarity measurement, to handle large amount of noise, to have better results in dealing the data corrupted by noise, and other artifacts. The clustering results of proposed methods are validated using Silhouette Method.