Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images

Dermoscopic rules such as the ABCD and Menzies rules are employed by dermatologists to determine the likelihood that a suspicious lesion is cancerous. This dissertation focuses on the improvement of automated melanoma recognition systems that implement these rules, specifically by enhancing the ability of these systems to recognize lesion asymmetry, a significant indicator of melanoma.

Two approaches are proposed for asymmetry classification. The first utilizes the irregularity of the outer contour of the lesion combined with measures that compare quadrants of the lesion with respect to area, color, and melanin content. The second method uses size theory as the basis for determining asymmetry. In this approach, measuring functions are employed to expose relevant characteristics of the lesion. The one-dimensional measuring functions are mapped into size functions in R 2 and compared using the bottleneck distance. The distances are used as features for classification.

Annotated dermoscopic images were used to train classifiers for both methods. Classification rates were competitive with other approaches for both methods independently, with the combined method exhibiting 95% accuracy.

Additionally, decision fusion strategies were investigated as a means of combining the results from individual melanoma classifiers using the asymmetry methods developed in this study. The best approach showed 100% sensitivity and 64% specificity, exceeding the performance of the individual classifiers.

Finally, a software framework for the development of medical applications is presented. This framework attempts to provide biomedical researchers with a simplified approach to creating mobile applications for medical processing.