Efficient SVM classifier based on color and texture region features for wound tissue images

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 6915, Issue , 2008, Pages

  Wannous, Hazem ^a   Lucas, Yves ^b   Treuillet, Sylvie ^b  

^a ENSI Bourges   (France)

^b UNIVERSITÉ D'ORLÉANS   (France)

Author keywords

Computer aided diagnosis; Feature extraction; Segmentation; Skin lesion; SVM classification

Indexed keywords

COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; MEDICAL PROBLEMS;

SKIN LESION; SVM CLASSIFICATION;

SKIN;

EID: 44349151697     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.770339     Document Type: Conference Paper

References (29)

1. M. Romanelli, G. Gaggio, A. Piaggesi, M. Coluccia and F. Rizello, "Technological advances in wound bed measurements," Wounds, vol. 14, no. 2, pp. 58-66, 2002.
2. B.F. Jones and P. Plassman, "An instrument to measure the dimensions of skin wounds", IEEE Trans, on Biomed. Eng., vol. 42, no. 5, pp. 464-470, May 1995.
3. H. Oduncu, A. Hoppe, M. Clark, R.J. Williams and K.G. Harding, "Analysis of skin wound images using digital color image processing: a preliminary communication", Lower Extremity Wounds, vol. 3, no. 3, pp. 151-156, 2004.
4. P. Plassman and D.J. Jones, "Improved active contour models with application to measurement of leg ulcers", Journal of Electronic Imaging, vol.12, no. 2, pp. 317-326, April 2003.
5. M Herbin, F X Bon, A Venot, F Jeanlouis, M L Dubertret, L Dubertret, and G Strauch, Assessment of healing kinetics through true color image processing, IEEE Transactions on Medical Imaging, vol. 12, no. 1, pp. 39-43, March 1993.
6. P. Plassman and B.F. Jones, "MAVIS: a non-invasive instrument to measure area and volume of wounds", Medical Engineering Physics, vol. 20, no. 5, pp. 332-338, 1998.
7. J. R. Mekkes and W. Westerhof, "Image processing in the study of wound healing," Clin. Dermatology, vol. 13, no. 4, pp. 401-407, 1995.
8. W.P. Berriss and S.J. Sangwine, "Color histogram clustering technique for tissue analysis of healing skin wound", in proceedings of 6th Int. Conf. on Image Processing and its Applications, Part 2, July 14-15, Dublin, Ireland, pp. 693-99, 1988.
9. A. Perez, A. Gonzaga, J. Alves, "Segmentation and analysis of leg ulcers color images", Medical Imaging and Augmented Reality, June 10-12, Hong Kong, pp. 262-266, 2001.
10. H. Zheng, L. Bradley, D. Patterson, M. Galushka, "New protocol for leg ulcer tissue classification from color images", IEEE EMBS, San Francisco, vol.2, pp. 1389-1392, 2004.
11. M. Kolesnik and A. Fexa, "Segmentation of Wounds in the Combined Color-Texture Feature Space", Medical Imaging, vol. 5370, pp. 549-556, 2004.
12. M. Galushka, H. Zheng, D. Patterson and L. Bradley, "Case-based tissue classification for monitoring leg ulcer healing", CBMS, pp. 353-358, 2005.
13. M. Callieri, P. Cignoni, M. Coluccia, G. Gaggio, P. Pingi, M. Romanelli and R. Scopigno, "Derma: monitoring the evolution of skin lesions with a 3D system", 8th Int. Workshop on Vision, Modeling and Visualization, November 19-21, Munich, pp. 167-174, 2003.
14. Y., Lucas, S., Treuillet, B., Albouy, H., Wannous, J. C., Pichaud,: "3D and color wound assessment using a simple digital camera", 9th Meeting of the European Pressure Ulcer Advisory Panel, Berlin, September. 2006.
15. B. Albouy, E. Koenig, S. Treuillet and Y. Lucas, "Accurate 3D structure measurements from two uncalibrated views," Lecture Notes in Computer Science, vol. Advanced Concepts for Intelligent Vision Systems, pp. 1111-1121, September 2006.
16. G. Y. Tian, D. Gledhill, D. Taylor and D. Clarke, "Color Correction for Panoramic Imaging", IV02, pp. 483-488, 2002.
17. Y. Deng and B.S. Manjunath, "Unsupervised segmentation of color textures regions in images and video, " IEEE Trans, on PAMI, vol. 23, no. 8, pp. 140-147, August 2001.
18. Priese L & Rehrmann V.:"Fast and robust segmentation of natural color scenes". In Proc. of Asian Conf. on Computer Vision, Hongkong (1998) 598-606
19. Comaniciu D. & Meer P. "Robust analysis of feature space: Color image segmentation". In Proceedings IEEE Conf. on CVPR, Puerto Rico (1997) 750-755
20. Felzenszwalb F.P. & Daniel P. "Efficient Graph-Based Image Segmentation". Int. Journal of Computer Vision 59-2 Sept. 2004, 167-181
21. H. Wannous, Y. Lucas, S. Treuillet "Classification couleur pilotée par la segmentation pour l'évaluation de la cicatrisation ", XXIe Colloque GRETSI Traitement du signal et des images, 11-14 septembre 2007 Troyes, France
22. H. Wannous, Y. Lucas, S. Treuillet "Supervised Tissue Classification from Color Images for a Complete Wound Assessment Tool", EMBS 2007: 29th annual international conference of IEEE engineering in medicine and biology society, Lyon, France, August 2007.
23. Y. Deng, C. Kenney, M. S. Moore, and B. S. Manjunath, "Peer group filtering and perceptual color quantization," in Proc. IEEE Int. Symp. Circuits Syst., vol. 4, 1999, pp. 21-24.
24. W.Y. Ma, Y.Deng, B.S. Manjunath, "Tools for texture/color based search of images," Human Vision and Electronic Imaging II, Feb. 1997, Proc. SPIE, Vol. 3016, p. 496-507.
25. J. Chen, T. N. Pappas, A. Mojsilovic, and B. Rogowitz, "Adaptive image segmentation based on color and texture," Proc. ICIP 2002, Rochester, New York, Sept. 2002
26. C. Carson, S. Belongie, H. Greenspan, J. "Malik, Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying", IEEE Transaction on Pattern Analysis and Machine Intelligence, v.24 n.8, p.1026-1038, August 2002
27. Vladimir N. Vapnik, "The Nature of Statistical Learning Theory". Springer, New York 1995
28. I. Tsochantaridis, T. Hofmann, T. Joachims, Y. Altun, "Support Vector Learning for Interdependent and Structured Output Spaces", ICML, 2004
29. H.-T. Lin and L. Li, "Novel Distance-Based SVM Kernels for Infinite Ensemble Learning," presented at The 12th International Conference on Neural Information Processing, Taipei, Taiwan, 2005.