A Boosting Fuzzy Clustering Technique for Image Segmentation based on an Initial Low-Class

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Abstract: Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes a boosting fuzzy clustering algorithm utilizing non- local self-similarity and a low-class prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and an initial low-class. The algorithm's ability to achieve satisfactory results while simultaneously significantly increasing efficiency is demonstrated by experiments on medical and synthetic images.