Melanoma decision support using lighting-corrected intuitive feature models

Skin lesion analysis using standard camera images has received limited attention from the scientific community due to its technical complexity and scarcity of data. The images are privy to lighting variations caused by uneven source lighting, and unconstrained differences in resolution, scale, and equipment. In this chapter, we propose a framework that performs illumination correction and feature extraction on photographs of skin lesions acquired using standard consumer-grade cameras. We apply a multi-stage illumination correction algorithm and define a set of high-level intuitive features (HLIF) that quantifies the level of asymmetry and border irregularity about a lesion. This lighting-corrected intuitive feature model framework can be used to classify skin lesion diagnoses with high accuracy. The framework accurately corrects the illumination variations and achieves high and precise sensitivity (95% confidence interval (CI), 73.1–73.5 %) and specificity (95% CI, 72.0–72.4%) using a linear support vector machine classifier with cross-validation trials. It exhibits higher test-retest reliability than the much larger state-of-the-art low-level feature set (95% CI, 78.1–79.7% sensitivity, 75.3–76.3% specificity). Combining our framework with these low-level features attains sensitivity (95% CI, 83.3–84.8%) and specificity (95% CI, 79.7–80.1%), which is more accurate and reliable than classification using the low-level feature set.