A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning.

Vascular structures of skin are important biomarkers in diagnosis and assessment of cutaneous conditions. Presence and distribution of lesional vessels are associated with specific abnormalities. Therefore, detection and localization of cutaneous vessels provide critical information towards diagnosis and stage status of diseases.

Quantifying the visual appearance of sunscreens applied to the skin using indirect computer image colorimetry.

Background: There is no accepted method to objectively assess the visual appearance of sunscreens on the skin.

Methods: We present a method for sunscreen application, digital photography, and computer analysis to quantify the appearance of the skin after sunscreen application. Four sunscreen lotions were applied randomly at densities of 0.

A feature fusion system for basal cell carcinoma detection through data-driven feature learning and patient profile.

Background: Basal cell carcinoma (BCC) is the most common skin cancer, which is highly damaging in its advanced stages. Computer-aided techniques provide a feasible option for early detection of BCC. However, automated BCC detection techniques immensely rely on handcrafting high-level precise features.

Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification.

Blood vessels are important biomarkers in skin lesions both diagnostically and clinically. Detection and quantification of cutaneous blood vessels provide critical information toward lesion diagnosis and assessment. In this paper, a novel framework for detection and segmentation of cutaneous vasculature from dermoscopy images is presented and the further extracted vascular features are explored for skin cancer classification.