You are in:Home/Publications/Moftah HM, Azar AT, Al-Shammari ET, NI, Hassanien AE, Shoman M (2014). Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation. Neural Computing and Applications, 24(7-8): 1917-1928. DOI 10.1007/s00521-013-1437-4. [ISI Indexed: Impact Factor: 1.763].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Moftah HM, Azar AT, Al-Shammari ET, NI, Hassanien AE, Shoman M (2014). Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation. Neural Computing and Applications, 24(7-8): 1917-1928. DOI 10.1007/s00521-013-1437-4. [ISI Indexed: Impact Factor: 1.763].
Authors: Hossam M. Moftah, Ahmad Taher Azar, Eiman Tamah Al-Shammari, Neveen I. Ghali, Aboul Ella Hassanien, Mahmoud Shoman
Year: 2014
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

Image segmentation is vital for meaningful analysis and interpretation of the medical images. The most popular method for clustering is k-means clustering. This article presents a new approach intended to provide more reliable magnetic resonance (MR) breast image segmentation that is based on adaptation to identify target objects through an optimization methodology that maintains the optimum result during iterations. The proposed approach improves and enhances the effectiveness and efficiency of the traditional k-means clustering algorithm. The performance of the presented approach was evaluated using various tests and different MR breast images. The experimental results demonstrate that the overall accuracy provided by the proposed adaptive k-means approach is superior to the standard k-means clustering technique.

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