Abstract

Data obtained from 102 skin lesions in vivo by spectroscopic oblique-incidence reflectometry were analyzed. The participating physicians initially divided the skin lesions into two visually distinguishable groups based on the lesions’ melanocytic conditions. Group 1 consisted of the following two cancerous and benign subgroups: (1) basal cell carcinomas and squamous cell carcinomas and (2) benign actinic keratoses, seborrheic keratoses, and warts. Group 2 consisted of (1) dysplastic nevi and (2) benign common nevi. For each group, a bootstrap-based Bayes classifier was designed to separate the benign from the dysplastic or cancerous tissues. A genetic algorithm was then used to obtain the most effective combination of spatiospectral features for each classifier. The classifiers, tested with prospective blind studies, reached statistical accuracies of 100% and 95% for groups 1 and 2, respectively. Properties that related to cell-nuclear size, to the concentration of oxyhemoglobin, and to the concentration of deoxyhemoglobin as well as the derived concentration of total hemoglobin and oxygen saturation were defined to explain the origins of the classification outcomes.

© 2004 Optical Society of America

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