Abstract
A novel level set based image segmentation method is proposed in this paper. After analyzing advantages and drawbacks of SBGFRLS and LSD model, we propose a way to combine local information and global information through utilizing weighted energy generated by SBGFRLS model to reduce segmentation error and accelerate curve evolution. The total energy of proposed method comprises of local energy term, length term and weighted global energy term. In our experiments, two alternative values for the coefficient of global term are 0.1 or −0.1. Experiments on images with noise and intensity inhomogeneities show that the proposed method is effective and more accurate than both SBGFRLS model and LSD model.
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References
Ilea, D.E., Whelan, P.F.: Color image segmentation using a self-initializing EM algorithm. In: Proceedings of the 6th IASTED International Conference on Visualization, Imaging, and Image Processing (2006)
Chen, K.M., Chen, S.Y.: Color texture segmentation using feature distributions. Pattern Recogn. Lett. 23(7), 755–771 (2002)
Pappas, T.N.: An adaptive clustering-algorithm for image segmentation. IEEE Trans. Sig. Process. 40(4), 901–914 (1992)
Junqing, C., Pappas, T.N., Mojsilovic, A., Rogowitz, B.E.: Adaptive perceptual color-texture image segmentation. IEEE Trans. Image Process. 14(10), 1524–1536 (2005)
Zoller, T., Hermes, L., Buhmann, J.M.: Combined color and texture segmentation by parametric distributional clustering. In: 16th International Conference on Pattern Recognition, Vol II, Proceedings (2002)
Ilea, D.E., Whelan, P.F.: CTex - An adaptive unsupervised segmentation algorithm based on color-texture coherence. IEEE Trans. Image Process. 17(10), 1926–1939 (2008)
Kato, Z., Pong, T.-C.: A Markov random field image segmentation model for color textured images. Image Vis. Comput. 24, 1103–1114 (2006)
Serrano, C., Acha, B.: Pattern analysis of dermoscopic images based on Markov random fields. Pattern Recogn. 42(6), 1052–1057 (2009)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. In: Proceedings of the 1st International Conference on Computer Vision (Cat. No.87CH2465-3) (1987)
Sundaramoorthi, G., Yezzi, A., Mennucci, A.C., Sapiro, G.: New possibilities with Sobolev active contours. Int. J. Comput. Vis. 84(2), 113–129 (2009)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vis. 46(3), 223–247 (2002)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50(3), 271–293 (2002)
Tsai, A., Yezzi, A., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process. 10(8), 1169–1186 (2001)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational-problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)
Chunming, L., Chenyang, X., Changfeng, G., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings (2005)
Li, C.M., Kao, C.Y., Gore, J.C., Ding, Z.H.: Implicit active contours driven by local binary fitting energy, In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1–8, 2007
Wang, L., He, L., Mishra, A., Li, C.M.: Active contours driven by local Gaussian distribution fitting energy. Sig. Process. 89(12), 2435–2447 (2009)
Wang, L., Wu, H., Pan, C.: Region-based image segmentation with local signed difference energy. Pattern Recogn. Lett. 34(6), 637–645 (2013)
Wang, Y., Wang, L.F., Xiang, S.M., Pan, C.H.: Level set evolution with locally linear classification for image segmentation. In: 2011 18th IEEE International Conference on Image Processing (2011)
Kaihua, Z., Lei, Z., Huihui, S., Wengang, Z.: Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis. Comput. 28(4), 668–676 (2010)
Shi, Y.G., Karl, W.C.: Real-time tracking using level sets. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol 2, Proceedings (2005)
Perona, P., Malik, J.: Scale-space and edge-detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Acknowledgement
This work is supported by National Natural Science Foundation of China under Grant NO. 61305051 and National High-Tech Research and Development Plan of China under Grant No. 2012AA01A306.
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Yang, C., Wu, W., Su, Y. (2014). Level Set Method Based Image Segmentation by Combining Local Information with Global Information. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2013. Communications in Computer and Information Science, vol 426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43908-1_43
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DOI: https://doi.org/10.1007/978-3-662-43908-1_43
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