K-means++ clustering-based active contour model for fast image segmentation

Yu Guo; Guirong Weng

doi:10.1117/1.JEI.27.6.063013

4 December 2018 K-means++ clustering-based active contour model for fast image segmentation

Yu Guo, Guirong Weng

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063013 (December 2018). https://doi.org/10.1117/1.JEI.27.6.063013

Abstract

Intensity inhomogeneity often occurs in real-life images because of nonuniform illumination, device operating, and technical limitation. We propose a K-means++ clustering-based active contour model for fast image segmentation. The key point is two fitting functions whose value computed by K-means++ clustering algorithm before level set function evolution. At first, we set up a rectangular local window. Two fitting functions represent the center points of brighter and darker subregions in the moving rectangular local windows. The method avoids repeating calculation of the fitting functions during curve evolution compared with the traditional region-based active contour models. Therefore, the proposed model has lower computational costs, and we can obtain correct segmentation results in less time and fewer iterations. The proposed model can efficiently segment images with intensity inhomogeneity. In addition, the experiments have proved that the proposed model has strong robustness to initialization.

Citation Download Citation

Yu Guo and Guirong Weng "K-means++ clustering-based active contour model for fast image segmentation," Journal of Electronic Imaging 27(6), 063013 (4 December 2018). https://doi.org/10.1117/1.JEI.27.6.063013

Received: 21 July 2018; Accepted: 7 November 2018; Published: 4 December 2018

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