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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 5 — Mar. 1, 2010
  • pp: 4434–4448

Semi-Supervised Subspace Learning for Mumford-Shah Model Based Texture Segmentation

Yan Nei Law, Hwee Kuan Lee, and Andy M. Yip  »View Author Affiliations


Optics Express, Vol. 18, Issue 5, pp. 4434-4448 (2010)
http://dx.doi.org/10.1364/OE.18.004434


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Abstract

We propose a novel image segmentation model which incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. While the natural unsupervised approach to learn a feature subspace can easily be trapped in a local solution, we propose a novel semi-supervised optimization algorithm that makes use of information derived from both the intermediate segmentation results and the regions-of-interest (ROI) selected by the user to determine the optimal subspaces of the target regions. Meanwhile, these subspaces are embedded into a Mumford-Shah objective function so that each segment of the optimal partition is homogeneous in its own subspace. The method outperforms standard Mumford-Shah models since it can separate textures which are less separated in the full feature space. Experimental results are presented to confirm the usefulness of subspace clustering in texture segmentation.

© 2010 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(100.5010) Image processing : Pattern recognition
(100.4994) Image processing : Pattern recognition, image transforms

ToC Category:
Image Processing

History
Original Manuscript: December 16, 2009
Revised Manuscript: February 5, 2010
Manuscript Accepted: February 6, 2010
Published: February 18, 2010

Citation
Yan Nei Law, Hwee Kuan Lee, and Andy M. Yip, "Semi-Supervised subspace learning for Mumford-Shah model based texture segmentation," Opt. Express 18, 4434-4448 (2010)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-5-4434


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References

  1. A. Kneller, "The new age of bioimaging," Paradigm 18, 18-25 (2006).
  2. P. Petrou and P. G. Sevilla, Dealing with Texture (Wiley, 2006). [CrossRef]
  3. D. Gabor, "Theory of communication," Proc. of J. IEE, (London) 93, 429-459 (1946).
  4. K. Laws, "Texture energy measures," Proc. of Image Understanding Workshop pp. 47-51 (1979).
  5. A. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters," Pattern. Recogn. 24, 1167-1186 (1991). [CrossRef]
  6. B. Manjunath and W. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Mach. Intell. 18, 837-842 (1996). [CrossRef]
  7. B. Sharif, A. Ahmadian, M. Oghabian, and N. Izadi, "Texture segmentation of endometrial images for aiding diagnosis of hyperplasia," Proceedings of the International Conference on Computer as a Tool 2, 983-986 (2005).
  8. M. Datar, D. Padfield, and H. Cline, "Color and texture based segmentation of molecular pathology images using HSOMs," Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 292-295 (2008). [CrossRef]
  9. S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color- and texture-based image segmentation using EM and its application to content-based image retrieval," Proc. IEEE Conf. on Computer Vision p. 675 (1998).
  10. A. Barbu and S. Zhu, "Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition," Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2, 731-738 (2004).
  11. N. Paragios and R. Deriche, "Geodesic active regions and level set methods for supervised texture segmentation," Int. J. Comput. Vision 46, 223-247 (2002). [CrossRef]
  12. B. Sandberg, T. Chan, and L. Vese, "A level-set and Gabor-based active contour algorithm for segmentation textured images," Cam report, UCLA (2002).
  13. C. Sagiv, N. Sochen, and Y. Zeevi, "Texture segmentation via a diffusion-segmentation scheme in the Gabor feature space," Proc. of the 2nd Intl. Workshop on Texture Analysis and Synthesis (2002).
  14. M. Rousson, T. Brox, and R. Deriche, "Active unsupervised texture segmentation on a diffusion based feature space," Proc. of the 2003 IEEE Computer Vision and Pattern Recognition (2003).
  15. T. Reed, "A review of recent texture segmentation and feature extraction techniques," CVGIP: Image Understanding 57, 359-372 (1993). [CrossRef]
  16. M. Haindl and S. Mikes, "Unsupervised texture segmentation," in Patten Recognition Techniques, Technology and Applications, P.-Y. Yin, ed., (I-Tech, 2008), pp. 227-248.
  17. D. Mumford and J. Shah, "Optimal approximation by piecewise smooth functions and associated variational problems," Commun. Pure Appl. Math. 42, 577-685 (1989). [CrossRef]
  18. S. Beucher and C. Lantuéjoul, "Use of watersheds in contour detection," Proc. International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation (1979). [PubMed]
  19. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision 1, 321-331 (1988). [CrossRef]
  20. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic subspace clustering of high dimensional data for data mining applications," Proc. of the 1998 ACM SIGMOD International Conference on Management of Data, pp. 94-105 (1998).
  21. C.-H. Cheng, A. W. Fu, and Y. Zhang, "Entropy-based subspace clustering for mining numerical data," Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 84-93 (1999). [CrossRef]
  22. C. Aggarwal and P. Yu, "Finding generalized projected clusters in high dimensional spaces," Proc. of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 70-81 (2000).
  23. J. Yang, W. Wang, H. Wang, and P. Yu, "d -clusters: capturing subspace correlation in a large data set," Proc. of 18th Interational Conference on Data Engineering, pp. 517-528 (2002). [CrossRef]
  24. J. Friedman, and J. Meulman, "Clustering objects on subsets of attributes," J. R. Statist. Soc. B 66, 815-849 (2004). [CrossRef]
  25. L. Jing, M. Ng, and J. Huang, "An entropy weighting k-means algorithm for subspace clustering of highdimensional sparse data," IEEE Trans. Knowledge and Data Engineering 19, 1026-1041 (2007). [CrossRef]
  26. C. Yap and H. Lee, "Identification of cell nucleus using a Mumford-Shah ellipse detector," Proc. of ISVC 1, 582-593 (2008).
  27. W. Yu, H. Lee, S. Hariharan, W. Bu, and S. Ahmed, "Level set segmentation of cellular images based on topological dependence," Proc. of ISVC 1, 540-551 (2008).
  28. W. Zhu, T. Chan, and S. Esedoglu, "Segmentation with depth: A level set approach," SIAM J. Sci. Comput. 28, 1957-1973 (2006). [CrossRef]
  29. T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Tran. Image Process. 10, 266-277 (2001). [CrossRef]
  30. L. A. Vese and T. F. Chan, "A multiphase level set framework for image segmentation using the Mumford and Shah model," Int. J. Comput. Vision 50, 271-293 (2002). [CrossRef]
  31. T. Chan, B. Sandberg, and L. Vese, "Active contours without edges for vector-valued images," J. Visual Commun. Image Representation 11, 130-141 (2000). [CrossRef]
  32. P. Brodatz, Textures: A photographic album for artists and designers (Dover, New York, 1996).
  33. Y. Law, H. Lee, and A. Yip, "A multiresolution stochastic level set method for Mumford-Shah image segmentation," IEEE Transactions on Image Processing 17, 2289-2300 (2008). [CrossRef] [PubMed]
  34. L. Roberts, J. Redan, and H. Reich, "Extraperitoneal endometriosis with catamenial pneumothoraces: A review of the literature," J. Soc. Laparoendoscopic Surgeons 7, 371-375 (2003). [PubMed]
  35. T. Brox, M. Rousson, R. Deriche, and J. Weickert, "Unsupervised segmentation incorporating color, texture, and motion," Proc. of the Intl. Conf. on Computer Analysis of Images and Patterns pp. 353-360 (2003). [CrossRef]
  36. C. Chiao and R. Hanlon, "Cuttlefish camouflage: Visual perception of size, contrast and number of white squares on artificial checkerboard substrata initiates disruptive coloration," J. Experimental Biology 204, 2119-2125 (2001). [PubMed]
  37. M. Rachidi, A. Chappard, C. Marchadier, C. Gadois, E. Lespessailles, and C. L. Benhamou, "Application of Laws’ masks to bone texture analysis: An innovative image analysis tool in osteoporosis," Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 1191-1194 (2008). [CrossRef]
  38. T. F. Chan, S. Esedoglu, and M. Nikolova, "Algorithms for finding global minimizers of denoising and segmentation models," SIAM J. Appl. Math. 66, 1632-1648 (2006). [CrossRef]
  39. A. Chambolle, "An algorithm for total variation minimization and applications," J. Math. Imaging Vision 20, 89-97 (2004). [CrossRef]
  40. J.-F. Aujol, G. Gilboa, T. Chan, and S. Osher, "Structure-texture image decomposition - modeling, algorithms, and parameter selection," Int. J. Comput. Vis. 67, 111-136 (2006). [CrossRef]
  41. X. Bresson, S. Esedoglu, P. Vandergheynst, J.-P. Thiran, and S. Osher, "Fast global minimization of the active contour/snake model," J. Math. Imaging Vis. 28, 151-167 (2007). [CrossRef]

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