An efficient FCM-based method for image refinement segmentation.

The conventional fuzzy c-means clustering (FCM) algorithm is sensitive to noise because no spatial information is taken into account. Many related algorithms reduce the influence of noise by adding local information to the objective function. However, there are still many problems, such as poor edge-preserving and anti-noise performance. This paper proposes an FCM-based method for image refinement segmentation to address the above problems effectively. We first take advantage of the pre-classification results of image sub-blocks as a new metric to measure the similarity of pixels and then combine the grayscale and spatial features of the local windows to vote and refine on these initial clustering results, which optimize the classification of pixels. Compared with existing algorithms, our algorithm can correct the misclassified pixels in the global segmentation and reserve image edge better. In addition, it is efficient for noisy image segmentation, which can maximize the recognition of noise and eliminate outliers. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]