Randy Hays Moss

TL;DR: A methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented and the issue of class imbalance is addressed using various sampling strategies and the classifier generalization error is estimated using Monte Carlo cross validation.

TL;DR: This work has shown that automated border detection is one of the most important steps in the computer‐aided diagnosis of melanoma, because the accuracy of the subsequent steps crucially depends on it.

TL;DR: Dermoscopy images of pigmented lesions are most commonly taken at × 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate to obtain accurate skin lesion segmentation from the background skin.

TL;DR: A machine learning approach to the detection of blue-white veil and related structures in dermoscopy images is presented, which involves contextual pixel classification using a decision tree classifier.

TL;DR: Asymmetry, a critical feature in the diagnosis of malignant melanoma, is analyzed using a new algorithm to find a major axis of asymmetry and calculate the degree of asymmetric of the tumor outline.