Computational vision systems for the detection of malignant melanoma.
In recent years, computational vision-based diagnostic systems for dermatology have demonstrated significant progress. We review these systems by first presenting the installation, visual features utilized for skin lesion classification and the methods for defining them. We also describe how to extract these features through digital image processing methods, i.e. segmentation, registration, border detection, color and texture processing, and present how to use the extracted features for skin lesion classification by employing artificial intelligence methods, i.e. discriminant analysis, neural networks, and support vector machines. Finally, we compare these techniques in discriminating malignant melanoma tumors versus dysplastic naevi lesions.
Maglogiannis
Ilias
I
Department of Information and Communication Systems Engineering, University of Aegean, 83200 Karlovasi, Samos, Greece. imaglo@aegean.gr
Kosmopoulos
Dimitrios I
DI
D001331:Automation; D003937:Diagnosis, Differential; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008545:Melanoma / Q000175:diagnosis*; D008962:Models, Theoretical; D010781:Photography; D012871:Skin Diseases / Q000175:diagnosis*; D012878:Skin Neoplasms / Q000175:diagnosis*