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Adaptive k-means clustering algorithm for MR breast image segmentation

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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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Authors and Affiliations

  1. Faculty of Computers and Information, Beni Suef University, Beni Suef, Egypt

    Hossam M. Moftah

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Hossam M. Moftah, Ahmad Taher Azar, Neveen I. Ghali & Aboul Ella Hassanien

  4. Faculty of Computing Science and Engineering, Kuwait University, Kuwait City, Kuwait

    Eiman Tamah Al-Shammari

  5. Faculty of Science, Al-Azhar University, Cairo, Egypt

    Neveen I. Ghali

  6. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien & Mahmoud Shoman

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  1. Hossam M. Moftah
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  2. Ahmad Taher Azar
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  3. Eiman Tamah Al-Shammari
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  5. Aboul Ella Hassanien
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  6. Mahmoud Shoman
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Correspondence to Ahmad Taher Azar.

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Moftah, H.M., Azar, A.T., Al-Shammari, E.T. et al. Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput & Applic 24, 1917–1928 (2014). https://doi.org/10.1007/s00521-013-1437-4

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  • DOI: https://doi.org/10.1007/s00521-013-1437-4

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