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The effectiveness of K-means clustering algorithm for image segmentation has  been proven in many studies, but is limited in the following problems: 1)  the determination of a proper number of clusters. If the number of clusters  is determined incorrectly, a good-quality segmented image cannot be  guaranteed; 2) the poor typicality of clustering prototypes; and 3) the  determination of an optimal number of pixels. The number of pixels plays an  important role in any image processing, but so far there is no general and  efficient method to determine the optimal number of pixels. In this paper, a  grid-based K-means algorithm is proposed for image segmentation. The  advantages of the proposed algorithm over the existing K-means algorithm  have been validated by some benchmark datasets. In addition, we further  analyze the basic characteristics of the algorithm and propose a general  index based on maximizing grey differences between investigated objective  grays and background grays. Without any additional condition, the proposed  index is robust in identifying an optimal number of pixels. Our experiments  have validated the effectiveness of the proposed index by the image results  that are consistent with the visual perception of the datasets.

Application of grid-based k-means clustering algorithm for optimal image processing