Malignant melanoma is by far the most dangerous type of skin cancer. Currently, the gold standard to diagnose melanoma in the clinic is excisional biopsy and histopathologic analysis. Approximately 15-30 benign lesions are biopsied to diagnose each melanoma. Additionally, biopsies are invasive and result in pain, anxiety, scarring and disfigurement of patients, and they can be a financial burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT) with its high-resolution and intermediate penetration depth can potentially provide required diagnostic information, noninvasively. We propose an image analysis algorithm, ‘optical properties extraction (OPE)’ that drastically improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluate the performance of the algorithm using several tissue-mimicking phantoms. We then test the OPE algorithm with sixty-nine human subjects and demonstrate that melanoma can be differentiated from benign nevi with 97% sensitivity, and 98% specificity.