Abstract
Skin cancer is the most commonly diagnosed type of cancer in people, regardless of age, gender, or race. One of the most common malignant skin cancers is melanoma, which is a dangerous proliferation of melanocytes. It is a well-known fact that early diagnosis of skin cancer is crucial and allows for successful treatment. Treatment of melanoma is not effective when melanoma is at an advanced stage. A widely used tool for the examination of skin lesions is a dermatoscope, which uses optic magnification to visualize features that are invisible to the naked eye. For a precise and objective diagnosis, there is a need for a computerized method for the removal and inpainting of hairs in image processing. In this study, we present an algorithm for the detection and inpainting of hairs in color dermoscopic images.
This scientific research was supported by the National Science Center as research project no. 2011/01/N/ST7/06783.
References
1. Argenziano G, Soyer HP, De Giorgio V, Piccolo D, Carli P, Delfino M, et al. Interactive atlas of dermoscopy. Milan: Edra Medical Publishing and New Media, 2000.Search in Google Scholar
2. Cancer Research UK. Skin cancer key facts. Available at: http://www.cancerresearchuk.org/. Accessed: 25 July 2012.Search in Google Scholar
3. Jaworek-Korjakowska J. Automatic detection of melanomas: an application based on the ABCD criteria. In: Ewa Piętka, Jacek Kawa, editors. Proceedings of Information Technologies in Biomedicine: Third International Conference, ITIB 2012, Gliwice, Poland, June 11–13, 2012. Lecture notes in Computer Science. Berlin, Heidelberg: Springer Verlag, 2012:S67–76.Search in Google Scholar
4. Jaworek-Korjakowska J. Wykorzystanie metod przetwarzania obrazów w rozpoznawaniu i diagnostyce czerniaka złośliwego. Pomiary Automat Robot 2011;12:100–1.Search in Google Scholar
5. Di Leo G, Paolillo A, Sommella P, Fabbrocini G, Rescigno O. A software tool for the diagnosis of melanomas. In: IEEE Instrumentation and Measurement Technology Conference (I2MTC), Austin, TX, USA, 2010;886–91.10.1109/IMTC.2010.5488165Search in Google Scholar
6. Celebi M, Kingravi H, Lee J. Fast and accurate border detection in dermoscopy images using statistical region merging. In: Proceedings of SPIE Medical Imaging 2007 Conference, San Diego, CA, 2007.10.1117/12.709073Search in Google Scholar
7. Bankman IN, editor. Handbook of medical image processing and analysis, 1st ed. New York: Academic Press, 2000.Search in Google Scholar
8. Seidenari S, Pellacani G, Giannetti A. Digital video microscopy and image analysis with automatic classification for detection of thin melanomas. Melanoma Res 1999; 9:163–72.10.1097/00008390-199904000-00009Search in Google Scholar
9. Seidenari S, Pellacani G, Grana C. Pigment distribution in melanocytic lesion images: a digital parameter to be employed for computer-aided diagnosis. Skin Res Technol 2005;11:236–41.10.1111/j.0909-725X.2005.00123.xSearch in Google Scholar
10. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB, 2nd ed. Knoxville, TN: Gatesmark Publishing, 2009.Search in Google Scholar
11. Hoshyar AN, Al-Jumaily A, Sulaiman R. Review on automatic early skin cancer detection. In: Dr. Tinghuai Ma, editor. International Conference on Computer Science and Service System (CSSS), Nanjing, China, June 2011. USA: Institute of Electrical and Electronics Engineers, Inc., 2011:4036–9.Search in Google Scholar
12. Di Leo G, Paolillo A, Sommella P, Fabbrocini G. Automatic diagnosis of melanoma: a software system based on the 7-point check-list. In: Sprague RH, Jr., editor. 43rd Hawaii International Conference on System Sciences (HICSS), Hawaii, 2010:1–10.Search in Google Scholar
13. Rigel DS, Friedman RJ, Kopf AW. The incidence of malignant melanoma in the United States: issues as we approach the 21st century. J Am Acad Dermatol 1996;34:839–47.10.1016/S0190-9622(96)90041-9Search in Google Scholar
14. Jaworek-Korjakowska J, Tadeusiewicz R. Assessment of asymmetry in dermoscopic colour images of pigmented skin lesions. In: Proceedings of the 10th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, February 2013.10.2316/P.2013.791-026Search in Google Scholar
15. Tadeusiewicz R. Preface: how intelligent should be system for image analysis? In: Kwasnicka H, Jain LC, editors. Innovations in intelligent image analysis. Studies in computational intelligence. Berlin: Springer Verlag, 2011:V.Search in Google Scholar
16. Tadeusiewicz R, Śmietański J. Acquisition of medical images and their processing, analysis, automatic recognition and diagnostic. Kraków: Wydawnictwo STN, 2011.Search in Google Scholar
©2013 by Walter de Gruyter Berlin Boston
