Application of Grid-based K-means Clustering Algorithm for Optimal Image Processing

Tingna Shi1, Penglong Wang1, Jeenshing Wangb2 and Shihong Yue1

  1. School of Electrical Engineering and Automation, Tianjin University
    Tianjin, China
  2. Department of Electrical Engineering, National Cheng Kung University
    Tainan 701, Taiwan


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.

Key words

electrical tomography; number of pixels; image ronconstruction

Digital Object Identifier (DOI)

Publication information

Volume 9, Issue 4 (December 2012)
Special Issue on Recent Advances in Systems and Informatics
Year of Publication: 2012
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Shi, T., Wang, P., Wangb, J., Yue, S.: Application of Grid-based K-means Clustering Algorithm for Optimal Image Processing. Computer Science and Information Systems, Vol. 9, No. 4, 1679-1696. (2012),