International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 1, No. 4, 29 Aug. 2011
Cover page and Table of Contents: PDF (size: 223KB)
Color Image Segmentation Using Level Set Method With Initialization Mask in Multiple Color Spaces
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Author(s)
Zhang Yongqin
1,*
Chen Hui
2
Wang Ling
3
Xiao Yongjun
4
Huang Haibo
3
Index Terms
Image segmentation, color space, level set method, Mumford-Shah framework
Abstract
The aim of image segmentation in imaging science is to solve the problem of partitioning an image into smaller disjoint homogeneous regions that share similar attributes. The improvement of level set method (LSM) based on Chan-Vese (C-V) model with initialization mask for vector image segmentation in multiple color spaces is studied here. And simultaneously, the final segmentation is completed by a simple labeling scheme. Then the comparative study of the refined C-V model is done in multiple color spaces. The experimental results illustrate that the optimized C-V model leads faster and better segmentation results with robustness to noise and good adaptability in RGB, CIE XYZ, and YCbCr color spaces where the results of test image changes little. But it has made mistakes in HSV and CIE L*a*b* color model. Moreover, these color spaces, i.e. h1h2h3, produce poor segmentation on the reliability and accuracy of a set of test images by performance analysis with evaluation indicators.
Cite This Paper
Zhang Yongqin,Chen Hui,Wang Ling,Xiao Yongjun,Huang Haibo,"Color Image Segmentation Using Level Set Method With Initialization Mask in Multiple Color Spaces", IJEM, vol.1, no.4, pp.70-76, 2011. DOI: 10.5815/ijem.2011.04.11
Reference
[1]Osher S, Sethian J A. Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations, Journal of Computational Physics, vol.79, pp.12-49, 1988.
[2]V Caselles, F Catté, T Coll, F Dibos. A Geometric Model for Active Contours. Numerische Mathematik, vol. 66, pp.1-31, 1993.
[3]R. Malladi, J.A. Sethian, B.C. Vemuri. Shape Modelling with Front Propagation: A Level Set Approach. IEEE Transactions on Pattern Analysis And Machine Intelligence, vol.17, no.2, pp.158-175, 1995.
[4]Faugeras O, Keriven R. Variational principles, surface evolution, PDEs, level set methods, and the stereo problem. IEEE Transactions on Image Processing, vol.7, no.3, pp.336-344, 1998.
[5]N Paragios, R Deriche. Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. International Journal of Computer Vision, vol.46, no.3, pp.223-247, 2002.
[6]Chan T F, Vese L A. Active contours without edges. IEEE Transactions on Image Processing, vol.10, no.2, pp.266 -277, 2001.
[7]Luminita A Vese, Tony F Chan. A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. International Journal of Computer Vision, vol.50, no.3, pp.271-293, 2002.
[8]D Cremers, S J Osher, S Soatto. Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision, vol.69, no.3, pp.335-351, 2006.
[9]J Sethian. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, Cambridge, UK, 2003.
[10]Hajihashemi M R, El-Shenawee M. Shape Reconstruction Using the Level Set Method for Microwave Applications. IEEE Antennas and Wireless Propagation Letters, vol.7, pp.92-96, 2008.
[11]D Mumford, J Shah. Optimal approximations by piecewise smooth functions and associated variational problems.Communications on Pure and Applied Mathematics, vol.42, pp.577-685, 1989.
[12]Chan T F, Sandberg Y B. Active contours without edges for vector-valued image. Journal of Visual Communication and Image Representation, vol.11, pp.130-141, 2000.