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Performance Analysis of proposed Hybrid FCM Algorithms with Standard FCM for Image Segmentation

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  • A. R. Jasmine Begum
  • T. Abdul Razak

Abstract

Segmentation is defined as an image that entails the division or separation of the image into regions of equal attribute. Clustering is one of the methods used for segmentation. Numerous algorithms using different approaches have been proposed for image segmentation. Clustering is an interesting approach for finding similarities in data and putting similar data into groups. Previous records indicate that clustering is a robust tool for acquiring classifications of image pixels. In this paper, the performance evaluation of the proposed Hybrid Fuzzy C-Means Cluster Center Estimation (HFCMCCE) , Enhanced Hybrid Fuzzy C-Means Cluster Center Estimation(EHFCMCCE) and Coherence Particle Swarm Optimization Algorithm with Specified Scrutiny of Fuzzy C-Means (CPSO-SSFCM) with standard FCM, is done based on the number of iterations taken to converge for clustering, Full Reference pixel based image quality measures PSNR,MSE, classification parameters such as Sensitivity, Specificity Accuracy, Extra Fraction(EF) and Similarity Index(SI).

Suggested Citation

  • A. R. Jasmine Begum & T. Abdul Razak, 2017. "Performance Analysis of proposed Hybrid FCM Algorithms with Standard FCM for Image Segmentation," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(5), pages 1001-1008, October.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i5:id:hcseit17262
    Note: Article URL: https://ijsrcseit.com/CSEIT17262
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