Skin Cancer Recognition by Computer Vision
Automatic detection of several features characteristic of basal cell epitheliomas is described. The features selected for this feasibility study are semitranslucency, telangiectasia, ulcer, crust, and tumor border. Image processing methods used in this study include frequency analysis of the Fourier transform of the image, the Sun-Wee texture analysis algorithm, and several other image analysis techniques suitable for skin photographs. This image analysis software is designed for use with AI/DERM, an expert system that models diagnosis of skin tumors by dermatologists.
R. H. Moss et al., "Skin Cancer Recognition by Computer Vision," Computerized Medical Imaging and Graphics, vol. 13, no. 1, pp. 31-36, Elsevier, Jan 1989.
The definitive version is available at https://doi.org/10.1016/0895-6111(89)90076-1
Electrical and Computer Engineering
National Science Foundation (U.S.)
Keywords and Phrases
Artificial Intelligence--Expert Systems; Computer Programming--Algorithms; Image Processing--Medical Applications; Computer Vision; Expert System AI/DERM; Skin Cancer Recognition; Skin Tumors; Sun-Wee Texture Analysis Algorithm; Biomedical Engineering; Artificial Intelligence; Computer Analysis; Fourier Transformation; Human; Image Processing; Methodology; Photography; Priority Journal; Skin Cancer; Carcinoma, Basal Cell; Diagnosis, Differential; Expert Systems; Feasibility Studies; Fourier Analysis; Image Interpretation, Computer-Assisted; Minicomputers; Pattern Recognition; Skin Neoplasms; Skin Ulcer; Support, U.S. Gov't, Non-P.H.S.; Telangiectasis; Basal Cell Carcinoma (Epithelioma); Fourier Transform Processing; Texture Analysis
International Standard Serial Number (ISSN)
Article - Journal
© 1989 Elsevier, All rights reserved.