Semi-automated diagnosis of melanoma through the analysis of dermatological images

Proceedings - 23rd SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2010

Volumn , Issue , 2010, Pages 71-78

  Parolin, Alessandro ^a   Herzer, Eduardo ^a   Jung, Cláudio R ^b  

^a UNIVERSIDADE DO VALE DO RIO DOS SINOS   (Brazil)

^b FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

Author keywords

Feature reduction; Image processing; Melanoma classification

Indexed keywords

ACCURACY RATE; DERMATOLOGICAL IMAGES; DETECTION RATES; EDGE MAP; FALSE NEGATIVE RATE; FALSE NEGATIVES; FALSE POSITIVE; FEATURE REDUCTION; MELANOMA CLASSIFICATION; NON-INVASIVE; PROBABILISTIC CLASSIFIERS; SEMI-AUTOMATED; SKIN CANCERS; SKIN LESION;

IMAGE PROCESSING; IMAGING SYSTEMS; ONCOLOGY; WAVELET TRANSFORMS;

DERMATOLOGY;

EID: 79952842911     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SIBGRAPI.2010.18     Document Type: Conference Paper

References (23)

1. H. Iyatomi, H. Oka, Celebi, M. Hashimoto, M. Hagiwara, M. Tanaka, and K. Ogawa, "An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm," Computerized Medical Imaging and Graphics, vol. 32, no. 7, pp. 566-579, October 2008.
2. J. H. Christensen, M. B. T. Soerensen, Z. Linghui, S. Chen, and M. O. Jensen, "Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion," Skin Research and Technology, vol. 16, 2010.
3. V. T. Devita, S. Hellman, and S. A. Rosenberg, Cancer: Principles & Practice of Oncology. Lippincott Williams & Wilkins, 2001.
4. A. A. Marghoob, L. D. Swindle, C. Z. M. Moricz, F. A. S. Negron, B. Slue, A. C. Halpern, and A.W. Kopf, "Instruments and new technologies for the in vivo diagnosis of melanoma," Journal of the American Academy of Dermatology, vol. 49, no. 5, pp. 777-797, 2003.
5. M. Barzegari, H. Ghaninezhad, P. Mansoori, A. Taheri, Z. Naraghi, and M. Asgari, "Computer-aided dermoscopy for diagnosis of melanoma," BMC Dermatology, vol. 5, no. 1, p. 8, 2005.
6. F. Ercal, A. Chawla, W. Stoecker, H.-C. Lee, and R. Moss, "Neural network diagnosis of malignant melanoma from color images," IEEE Transactions on Biomedical Engineering, vol. 41, no. 9, pp. 837-845, Sep 1994.
7. B. Kusumoputro and A. Ariyanto, "Neural network diagnosis of malignant skin cancers using principal component analysis as a preprocessor," in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 1, May 1998, pp. 310-315 vol.1.
8. I. Maglogiannis, S. Pavlopoulos, and D. Koutsouris, "An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 1, pp. 86-98, March 2005.
9. Z. Zhang, R. H. Moss, and W. V. Stoecker, "Neural networks skin tumor diagnostic system," in Proceedings of the International Conference on Neural Networks and Signal Processing, vol. 1, Dec. 2003, pp. 191-192 Vol.1.
10. C. Ebner, G. Gabler, C. Massone, R. Hofmann-Wellenhof, G. P. Lozzi, E. Wurm, and H. P. Soyer, "Mobile teledermatology coming of age," e & i Elektrotechnik und Informationstechnik, vol. 123, pp. 148-151, 2006.
11. R. C. Gonzalez and R. E. Woods, Digital Image Processing (2nd Edition). Prentice Hall, January 2002.
12. V. B. Medina, R. Valdes, O. Yanez-Suarez, M. Garza-Jinich, and J.-F. Lerallut, "Automatic initialization for a snakes-based cardiac contour extraction," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, 2000, pp. 1625-1628 vol.3.
13. J. Tang, S. Millington, S. Acton, J. Crandall, and S. Hurwitz, "Ankle cartilage surface segmentation using directional gradient vector flow snakes," in Proceedings of the IEEE International Conference on Image Processing, vol. 4, Oct. 2004, pp. 2745-2748 Vol. 4.
14. L. Chen, X.-L. Zhang, Z. Wang, and L.-T. Fang, "Contour extraction based on improved gvf snake model in asar images," in Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, vol. 1, Nov. 2007, pp. 301-304.
15. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. V1, no. 4, pp. 321-331, January 1988.
16. C. Xu and J. Prince, "Gradient vector flow: A new external force for snakes," in Proceedings of Computer Vision and Pattern Recognition (CVPR '97). San Juan, Puerto Rico: IEEE, June 1997, pp. 66-71.
17. S. G. Mallat and S. Zhong, "Characterization of signals from multiscale edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 7, pp. 710-732, 1992.
18. R. J. Friedman, D. S. Rigel, and A. W. Kopf, "Early detection of malignant melanoma: The role of physician examination and self-examination of the skin," CA Cancer J Clin, vol. 35, no. 3, pp. 130-151, 1985.
19. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd Edition). Wiley-Interscience, November 2000.
20. S. Zhang and T. Sim, "Discriminant subspace analysis: A Fukunaga-Koontz approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1732-1745, 2007.
21. D. Pena and F. J. Prieto, "Multivariate outlier detection and robust covariance matrix estimation," Technometrics, vol. 43, pp. 286-310(25), 2001.
22. C. R. Jung and J. Scharcanski, "Sharpening dermatological color images in the wavelet domain," IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 1, pp. 4-13, Feb. 2009.
23. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, October 1999.