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Application of Improved FCM Algorithm in Brain Image Segmentation

  • Manzhuo Yin
  • Jinghuan GuoEmail author
  • Yuankun Chen
  • Yong Mu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

Aiming at the problems of fuzzy c-means clustering (FCM) and its improved algorithms for MRI image segmentation, this paper proposes a new FCM algorithm based on neighborhood pixel correlation. The algorithm works out the influence degree of the neighborhood pixels on the central pixel by the correlation of the gray-level difference between the domain pixel and the center pixel. Then, the distance between the neighborhood pixel and the cluster center is used to control the membership of the center pixel, the improved algorithm will solve the existing influence factors of unification, ignoring the difference between pixels, resulting in inaccuracy of segmentation results. At last, this algorithm is implemented by MATLAB tool and compared with FCMS and FLICM algorithms. The feasibility of the presented algorithm and the accuracy of the segmentation result are verified by evaluating the algorithm and the experimental results according to the relevant evaluation criteria.

Keywords

Brain MRI image FCM improvement algorithm Pixel gray correlation Dissimilarity coefficient 

Notes

Acknowledgments

Fundamental Research Funds for the Central Universities (No. 3132018194). Project name: Research on Ship Scheduling Method Based on Swarm Intelligence Hybrid Optimization Algorithm.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Manzhuo Yin
    • 1
  • Jinghuan Guo
    • 1
    Email author
  • Yuankun Chen
    • 1
  • Yong Mu
    • 1
  1. 1.College of Information Science and TechnologyDalian Maritime UniversityDalian CityChina

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