We present a machine learning algorithm that can imitate the clinicians qualitative and visual process of analyzing reflectance confocal microscopy (RCM) mosaics at the dermal epidermal junction (DEJ) of skin. We divide the mosaics into localized areas of processing, and capture the textural appearance of each area using dense Speeded Up Robust Feature (SURF). Using these features, we train a support vector machine (SVM) classifier that can distinguish between meshwork, ring, clod, aspecific and background patterns in benign conditions and melanomas. Preliminary results on 20 RCM mosaics labeled by expert readers show classification with 55 − 81% sensitivity and 81 − 89% specificity in distinguishing these patterns.
Kivanc Kose, Christi Alessi-Fox, Melissa Gill, Jennifer G. Dy, Dana H. Brooks, and Milind Rajadhyaksha, "A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo," Proc. SPIE 9689, Photonic Therapeutics and Diagnostics XII, 968908 (Presented at SPIE BiOS: February 13, 2016; Published: 29 February 2016); https://doi.org/10.1117/12.2212978.
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