Characterization of digital medical images utilizing support vector machines.
BACKGROUND
In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study.


METHODS
The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared.


RESULTS
The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same.


CONCLUSIONS
The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis.
Maglogiannis
Ilias G
IG
University of the Aegean, Dept of Information and Communication Systems Engineering, Karlovasi, Greece. imaglo@aegean.gr
Zafiropoulos
Elias P
EP
D000465:Algorithms; D004416:Dysplastic Nevus Syndrome / Q000175:diagnosis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000175:diagnosis; D016571:Neural Networks, Computer; D012878:Skin Neoplasms / Q000145:classification / Q000175:diagnosis