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

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.