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 0.5, 1.0, 1.5, and 2.0 mg/cm2 to areas of the back of 29 subjects. Each application site had a matched contralateral control area. High-resolution standardized photographs including a color card were taken after sunscreen application. After color balance correction, CIE L*a*b* color values were extracted from paired sites. Differences in skin appearance attributed to sunscreen were represented by ΔE, which in turn was calculated from the linear Euclidean distance within the L*a*b* color space between the paired sites. RESULTS: Sunscreen visibility as measured by median ΔE varied across different products and application densities and ranged between 1.2 and 12.1. The visibility of sunscreens varied according to product SPF, composition (organic vs inorganic), presence of tint, and baseline b* of skin (P < .05 for all). CONCLUSION: Standardized sunscreen application followed by digital photography and indirect computer-based colorimetry represents a potential method to objectively quantify visibility of sunscreen on the skin.

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. However, cutaneous vessels are highly variable in shape, size, color and architecture, which complicate the detection task. Considering the large variability of these structures, conventional vessel detection techniques lack the generalizability to detect different vessel types and require separate algorithms to be designed for each type. Furthermore, such techniques are highly dependent on precise hand-crafted features which are time-consuming and computationally inefficient. As a solution, we propose a data-driven feature learning framework based on stacked sparse auto-encoders (SSAE) for comprehensive detection of cutaneous vessels. Each training image is divided into small patches of either containing or non-containing vasculature. A multilayer SSAE is designed to learn hidden features of the data in hierarchical layers in an unsupervised manner. The high-level learned features are subsequently fed into a classifier which categorizes each patch into absence or presence of vasculature and localizes vessels within the lesion. Over a test set of 3095 patches derived from 200 images, the proposed framework demonstrated superior performance of 95.4% detection accuracy over a variety of vessel patterns; outperforming other techniques by achieving the highest positive predictive value of 94.7%. The proposed Computer-Aided Diagnosis (CAD) framework can serve as a decision support system assisting dermatologists for more accurate diagnosis, especially in teledermatology applications in remote areas.

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. Given a dermoscopy image, we segment vascular structures of the lesion by first decomposing the image using independent-component analysis into melanin and hemoglobin components. This eliminates the effect of pigmentation on the visibility of blood vessels. Using k-means clustering, the hemoglobin component is then clustered into normal, pigmented, and erythema regions. Shape filters are then applied to the erythema cluster at different scales. A vessel mask is generated as a result of global thresholding. The segmentation sensitivity and specificity of 90% and 86% were achieved on a set of 500 000 manually segmented pixels provided by an expert. To further demonstrate the superiority of the proposed method, based on the segmentation results, we defined and extracted vascular features toward lesion diagnosis in basal cell carcinoma (BCC). Among a dataset of 659 lesions (299 BCC and 360 non-BCC), a set of 12 vascular features are extracted from the final vessel images of the lesions and fed into a random forest classifier. When compared with a few other state-of-art methods, the proposed method achieves the best performance of 96.5% in terms of area under the curve (AUC) in differentiating BCC from benign lesions using only the extracted vascular features.