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Abstrakty
Skin cancer is the most commonly diagnosed type of cancer in humans regardless of age, gender, or race. One of the most common malignant skin cancers is melanoma, which is a dangerous proliferation of melanocytes. In the last several years, an increasing melanoma incidence has been observed worldwide, and the incidence rate is increasing faster than those of any other skin cancer. The correct identification and diagnosis of moles still creates problems to inexperienced dermatologists and family physicians. In this paper, we present a new approach to the problem of assessing difficult cases in dermatology. We propose a teledermatology system to support the consultation process between family physicians and experts in the field of dermoscopic images. The system consists of a desktop monitoring application and a special smartphone application implemented for experts. If necessary, the physician can send the dermoscopic image to two dermatologists for further examination. This cloud-based architecture provides an interesting system for a fast and efficient exchange of dermatological information. Initial results and assessment of doctors are promising and indicate that the application can be used as a decision support system for dermoscopic images.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
53--58
Opis fizyczny
Bibliogr. 14 poz., rys., wykr., zdj.
Twórcy
autor
- Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow 30-059, Poland
autor
- Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow 30-059, Poland
Bibliografia
- 1. Di Leo G, Paolillo A, Sommella P, Fabbrocini G, Rescigno O. A software tool for the diagnosis of melanomas. Automatic implementation of the 7-point check list method. IEEE Instrum Measure Technol Conf (I2MTC) 2010:886–91.
- 2. Jaworek-Korjakowska J. Automatic detection of melanomas: an application based on the ABCD criteria. Proc Lecture Notes Comput Sci 2012;7339:67–76.
- 3. National Cancer Register, 2013. Available at: http://www.epid.coi.waw.pl/krn/. Accessed on 12 October 2013.
- 4. Cancer Research UK. Skin cancer key facts, 2012. Available at: http://www.cancerresearchuk.org/. Accessed on 5 May 2012.
- 5. Argenziano G, Soyer HP, De Giorgi V, Piccolo D, Carli P, Delfino M, et al. Interactive atlas of dermoscopy. Milan, Italy: EDRA Medical Publishing & New Media, 2002.
- 6. Schreier G, Hayn D, Kastner P, Koller S, Salmhofer W, Hofmann-Wellenhof R. A mobile-phone based teledermatology system to support self-management of patients suffering from psoriasis. Conf Proc IEEE Eng Med Biol Soc 2008;2008:5338–41.
- 7. Doukas C, Stagkopoulos P, Kiranoudis CT, Maglogiannis I. Automated skin lesion assessment using mobile technologies and cloud platforms. Conf Proc IEEE Eng Med Biol Soc 2012;2012:2444–7.
- 8. Derm101 Suite of Mobile Apps, 2013. Available at: http://www.derm101.com/. Accessed on 12 October 2013.
- 9. Jaworek-Korjakowska J, Tadeusiewicz R. Assessment of dots and globules in dermoscopic color images as one of the 7-point check list criteria. In: 20th IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, September 15–18, 2013:1456–60.
- 10. Jaworek-Korjakowska J, Tadeusiewicz R. Determination of border irregularity in dermoscopic color images of pigmented skin lesions. Conf Proc IEEE Eng Med Biol Soc 2014;2014: 6459–62.
- 11. Kmiec M, Glowacz A. Object detection in security applications using dominant edge directions. Pattern Recognit Lett 2015;52:72–9.
- 12. Tadeusiewicz R, Śmietański J. Acquisition of medical images and their processing, analysis, automatic recognition and diagnostic. Wydawnictwo STN, Krakow, 2011.
- 13. Głowacz A, Głowacz A, Głowacz Z. Diagnostics of direct current generator based on analysis of monochrome infrared images with the application of cross-sectional image and nearest neighbor classifier with Euclidean distance. Przegląd Elektrotechniczny 2012;88:154–7.
- 14. Jaworek-Korjakowska J, Tadeusiewicz R. Hair removal from dermoscopic color images. Bio-Algorithms Med-Syst 2013;9:53–8.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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