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.
Moss
R H
RH
Department of Electrical Engineering, University of Missouri, Rolla 65401.
Stoecker
W V
WV
Lin
S J
SJ
Muruganandhan
S
S
Chu
K F
KF
Poneleit
K M
KM
Mitchell
C D
CD
D002280:Basal Cell Carcinoma / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005103:Expert Systems; D005240:Feasibility Studies; D005583:Fourier Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008905:Minicomputers; D010363:Pattern Recognition, Automated; D010781:Photography; D012878:Skin Neoplasms / Q000175:diagnosis*; D012883:Skin Ulcer / Q000175:diagnosis; D013684:Telangiectasis / Q000175:diagnosis