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Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search

Author

Listed:
  • E. Emary

    (Cairo University
    Scientific Research Group in Egypt (SRGE))

  • Hossam M. Zawbaa

    (Beni-Suef University
    Babes-Bolyai University)

  • Aboul Ella Hassanien

    (Cairo University
    Scientific Research Group in Egypt (SRGE))

  • B. Parv

    (Babes-Bolyai University)

Abstract

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

Suggested Citation

  • E. Emary & Hossam M. Zawbaa & Aboul Ella Hassanien & B. Parv, 2017. "Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 611-627, September.
    • Handle: RePEc:spr:advdac:v:11:y:2017:i:3:d:10.1007_s11634-016-0257-7
    DOI: 10.1007/s11634-016-0257-7
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    References listed on IDEAS

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    1. Doulaye Dembélé, 2008. "Multi-objective optimization for clustering 3-way gene expression data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 2(3), pages 211-225, December.
    2. Zhiliang Liu & Xiaomin Zhao & Ming Zuo & Hongbing Xu, 2014. "Feature selection for fault level diagnosis of planetary gearboxes," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 377-401, December.
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    Cited by:

    1. Ying Sun & Yuelin Gao & Xudong Shi, 2019. "Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity," Mathematics, MDPI, vol. 7(2), pages 1-16, February.

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