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    This study introduces a robust fuzzy C-means (FCM) algorithm for improved image segmentation. The enhanced method combines local information and adaptive gray weighting for superior precision and efficiency.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Traditional fuzzy C-means (FCM) algorithms and spatial-based extensions suffer from poor robustness and low accuracy due to limited local information utilization.
    • FCM's initial center estimation often involves inefficient searching within the gray range, impacting performance.

    Purpose of the Study:

    • To develop a novel, robust algorithm for image segmentation that overcomes the limitations of traditional FCM methods.
    • To enhance segmentation accuracy and efficiency by effectively incorporating local information and adaptive gray weighting.

    Main Methods:

    • Introduced an improved parallel Lévy grey wolf optimization algorithm for optimal initial center estimation.
    • Developed an enhanced fuzzy C-means segmentation algorithm integrating local information and adaptive gray weighting.

    Main Results:

    • The proposed algorithm demonstrates superior robustness and segmentation accuracy compared to existing methods.
    • Experimental results confirm significant improvements in both precision and efficiency.

    Conclusions:

    • The novel FCM-based algorithm effectively addresses the limitations of traditional methods.
    • The integration of improved optimization and adaptive weighting significantly enhances image segmentation performance.