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Analyse melanozytaerer Laesionen mit Hilfe der Farbbildauswertung

By H. Harms, W. Stolz, V. ter Meulen, L. Pillet, R. Albert, T. Schindewolf, W. Abmayr, R. Schiffner, Mathematik Muenchen Fachhochschule (Germany). Fachbereich Informatik, Regensburg Univ. (Germany). Klinik und Poliklinik fuer Dermatologie and Wuerzburg Univ. (Germany). Inst. fuer Virologie und Immunbiologie

Abstract

In the central and northern parts of Europe 10 to 15 of 100000 people fall ill per year with a malignant melanoma. The tendency is still increasing. In Germany this results in an incidence of around 4000 to 8000 people. Well trained and experienced dermatologists are able to reach a diagnostic accuracy of 70% to 80% in visual classification of malignant melanoma. Therefore we attempted to improve the early detection and diagnosis with computer aided methods. According to the visual dermatologic classification criteria (ABCD-rule) image processing methods were developed to determine features describing color, texture, asymmetry and border. A correct classification of 90% to 92% was reached. The data show that the results of the computer aided analysis are nearly 20% better than the visual human classification. More than 1200 pigmented skin lesions (benign and malignant) were scanned and analyzed with an image processing system. The system consisting of a RGB-TV camera and a reproducible feature extraction may lead to a higher quality in treatment and early detection of melanoma. The clinical evaluation and the inclusion of other pigmented lesions like navoid lentigo, acuminate nevus, seborrheic verruca senilis and basal cell epithelioma are not yet done. (orig.)SIGLEAvailable from TIB Hannover: F94B1219+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Forschung und Technologie (BMFT), Bonn (Germany)DEGerman

Topics: 06E - Medicine, 09K - Pattern recognition, image processing, MELANOMAS: M, DIAGNOSIS: M, IMAGE SCANNERS: M, IMAGE ANALYSIS, DATABASE MANAGEMENT, FOURIER TRANSFORMATION, PATTERN RECOGNITION, COMPUTERIZED SIMULATION
Year: 1994
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Provided by: OpenGrey Repository
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