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An Improved Fuzzy Clustering Segmentation Algorithm Based on Animal Behavior Global Optimization

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

Abstract

The bio-inspired optimization algorithms play vital role in many research domains and this work analyzes animal behavior optimization algorithm. Medical image segmentation helps the physicians for disease diagnosis and treatment planning. This work incorporates ABO algorithm for cluster centroid selection in Fuzzy C-means clustering segmentation algorithm. The Animal Behavior Optimization (ABO) algorithm was developed based on the group behavior and was validated on 13 benchmark functions. The dominant nature of an animal species decides the fitness function value and each solution in problem space depicts the animal position. The ABO algorithm was coupled with the classical FCM for the analysis of region of interest in abdomen CT and brain MR datasets. The results were found to be efficient when compared with the FCM coupled with artificial bee colony (ABC), firefly, and cuckoo optimization algorithms. The promising results generated by ABC makes it an efficient one for real-world problems.

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Acknowledgements

The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015).

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Authors and Affiliations

  1. School of Electronics and Communication Engineering, Mar Ephraem College of Engineering and Technology, Marthandam, Kanyakumari, India

    A. Absara, S. N. Kumar, H. Ajay Kumar & V. Suresh

  2. School of Computer Science and Engineering, Mar Ephraem College of Engineering and Technology, Marthandam, Kanyakumari, India

    A. Lenin Fred

Authors
  1. A. Absara
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  2. S. N. Kumar
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  3. A. Lenin Fred
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  4. H. Ajay Kumar
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  5. V. Suresh
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Corresponding author

Correspondence to A. Absara .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Absara, A., Kumar, S.N., Lenin Fred, A., Ajay Kumar, H., Suresh, V. (2020). An Improved Fuzzy Clustering Segmentation Algorithm Based on Animal Behavior Global Optimization. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_60

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