Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature

Malignant melanoma (MM) is one of the most frequent types of cancers among the world's white population. Dermoscopy is a noninvasive method for early recognition of MM by which physicians assess the skin lesion according to the skin subsurface features. The presence or absence of “streaks” is one of the most important dermoscopic criteria for the diagnosis of MM. We develop a machine-learning approach for identifying streaks in dermoscopic images using a novel melanoma feature, which captures the quaternion tubularness in the color dermoscopic images, is sensitive to the radial features of streaks, and is localized to different lesion bands (e.g. the most periphery band where streaks commonly appear). We validate the classification accuracy of SVM using our novel features on 99 dermoscopic images (including images in the absence, presence of regular, and presence of irregular streaks). Compared to state-of-the-art, we obtain improved classification results by up to 9% in terms of area under ROC curves.