Traditional methods for early detection of melanoma rely upon a dermatologist to visually assess a skin lesion using the ABCDE (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria before confirmation can be done through biopsy by a pathologist. However, this visual assessment strategy taken by dermatologists is hampered by clinician subjectivity and suffers from low sensitivity. Computer-aided diagnostic methods based on dermatological photographs are being developed to aid in the melanoma diagnosis process, but most of these methods rely only on superficial, topographic features that can be limiting in characterizing melanoma. In this work, a hybrid feature model is introduced for characterizing skin lesions that combines low-level and high-level features, and augments them with a set of physiological features extracted from dermatological photographs using a nearest-neighbor nonlinear model to improve classification performance. The physiological features extracted from the lesion for the proposed hybrid feature model include those based on: i) eumelanin concentrations, ii) pheomelanin concentrations, and iii) blood oxygen saturation. The proposed hybrid feature model was evaluated on 206 dermatological photographs of skin lesions (119 confirmed melanoma cases, 87 confirmed non-melanoma cases) using a cross validation scheme. The experimental results show that the proposed hybrid feature model, with integrated physiological features, provided improved sensitivity, specificity, precision and accuracy for the purpose of melanoma classification.