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Lyme borreliosis is the most common human tick-borne infectious disease in the northern hemisphere, occurring predominantly in temperate regions of North America, Europe and Asia. The disease’s most frequent manifestation is erythema migrans, a skin lesion that appears within days to weeks of a tick bite. Early recognition of the lesion is important since it enables proper management and thus prevention of later consequences of the disease which can hamper normal life. In this article, a novel visual system for recognition of erythema migrans is presented based on new technology of smartphones. For detecting erythema migrans edge, we compared three different methods: GrowCut, Maximal Similarity Based Region Merging and Random Walker segmentation method. We have found that the results obtained with GrowCut method are better than those obtained with Random Walker method. Also the GrowCut method, improved with our new figure draw (FD1) marker yields comparable results to those obtained with Maximal Similarity Based Region Merging method. Several classification algorithms Naive Bayes, Support Vector Machine, Adaboost, Random forest and Neural network were compared and used for classification of skin lesions into ellipse, the most common shape of erythema migrans and erythema migrans class.

Supervised Visual System for Recognition of Erythema Migrans, an Early Skin Manifestation of Lyme Borreliosis