Capillary detection for clinical images of basal cell carcinoma

Dilated capillaries are an important characteristic of basal cell carcinoma (BCC). Detecting capillaries in images can improve a computer-aided skin cancer diagnosis system. In this study, we investigate the feasibility to extract capillaries from clinical images of skin lesions recorded by a regular digital camera. First, we used a compact set of 1 curvilinear and 2 color parameters to train a support vector machine (SVM) classifier to identify capillary pixels. Second, the identified pixels were grouped by a region-growing algorithm to form capillary candidates. Last, the likelihood to be a true capillary was estimated based on the distance to the red color in the CIE Lab color space. The method was tested on a dataset of 21 BCC images with visible capillaries and 28 benign pigmented lesions without visible capillaries. The accuracy, sensitivity, and specificity of the proposed method were 89.8% (44/49), 90.5% (19/21), and 89.3% (25/28) respectively. We found capillaries recorded by a regular digital camera can be detected successfully.