An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation

• Published in: IEEE transactions on image processing Vol. 18; no. 8; pp. 1844 - 1858
• Main Authors: Wang, Jie, Ju, Lili, Wang, Xiaoqiang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active contours; Algorithms; Applied sciences; Biological system modeling; centroidal voronoi tessellations; Cluster Analysis; clustering; Clustering algorithms; Computer vision; Density; Density functional theory; edge detection; Exact sciences and technology; Image edge detection; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Information, signal and communications theory; Mathematical models; Models, Statistical; Partial differential equations; Partitioning algorithms; Pattern recognition; Robustness; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Tessellation; Performance evaluation; Automatic classification; Voronoï diagram; Image processing; centroidal voronoi tessellations; Algorithm; Signal classification; Shape detection; Flexibility; Implementation; Active contours; Image segmentation; Algorithm performance; Center of mass; Density function; Robustness; clustering; Edge detection; Active contour
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2009.2021087

Centroidal Voronoi tessellations (CVTs) are special Voronoi tessellations whose generators are also the centers of mass (centroids) of the Voronoi regions with respect to a given density function and CVT-based methodologies have been proven to be very useful in many diverse applications in science and engineering. In the context of image processing and its simplest form, CVT-based algorithms reduce to the well-known k -means clustering and are easy to implement. In this paper, we develop an edge-weighted centroidal Voronoi tessellation (EWCVT) model for image segmentation and propose some efficient algorithms for its construction. Our EWCVT model can overcome some deficiencies possessed by the basic CVT model; in particular, the new model appropriately combines the image intensity information together with the length of cluster boundaries, and can handle very sophisticated situations. We demonstrate through extensive examples the efficiency, effectiveness, robustness, and flexibility of the proposed method.