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Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation.

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    |December 1, 2022
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    Summary

    Accurate noise estimation improves Fuzzy C-Means (FCM) image segmentation. Residual-driven FCM (RFCM) offers superior noise estimation over deviation-sparse FCM (DSFCM), especially for complex noise types.

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    Area of Science:

    • Image Processing
    • Computer Vision
    • Pattern Recognition

    Background:

    • Noisy images degrade image segmentation quality.
    • Fuzzy C-Means (FCM) segmentation is sensitive to noise.
    • Accurate noise estimation is crucial for enhancing FCM robustness.

    Purpose of the Study:

    • To comparatively analyze deviation-sparse FCM (DSFCM) and residual-driven FCM (RFCM) for image segmentation.
    • To evaluate the effectiveness of RFCM in accurate noise estimation across various noise types.
    • To demonstrate the superiority of RFCM over DSFCM in handling diverse noise conditions.

    Main Methods:

    • Comparative analysis of DSFCM and RFCM algorithms.
    • Utilizing noise distribution characteristics for estimation in RFCM.
    • Implementing spatial information constraints within the RFCM framework.
    • Experimental validation with single, mixed, and unknown noise levels.

    Main Results:

    • RFCM achieves more accurate noise estimation than DSFCM.
    • DSFCM is identified as a specific case of RFCM, equivalent under impulse noise.
    • RFCM demonstrates superior effectiveness and efficiency compared to DSFCM.
    • RFCM's performance is validated across various noise types and levels.

    Conclusions:

    • RFCM provides a more robust and accurate approach to noise estimation in FCM-based image segmentation.
    • The RFCM framework offers enhanced performance over DSFCM, particularly for complex and mixed noise.
    • RFCM's ability to leverage noise distribution characteristics is key to its improved accuracy.