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

Erik Čuk,Matjaž Gams,Jurij F Tasič,Matej Možek,Franc Strle,Vera Maraspin Čarman

doi:10.5545/sv-jme.2013.1046

Abstract

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.

Highlights

For machine-supported detection of segments of interest in medical images and understanding the content with learning algorithms [1] and [2], neural networks [3] and [4], naive Bayes, support vector machine (SVM) and others [5] are among key diagnostic tools of modern medicine
In the third section besides segmentation results for all methods, we present the results in combination with maximal similarity based region merging (MSRM)-finger draw 2” (FD2) and GC-finger draw 1” (FD1) segmentation methods related to erythema migrans computer- aided diagnosis
The novelty of our approach stems from the use of pattern recognition methods in the field of infectious diseases including erythema migrans, development of new features that can be used for medical purposes, and from the mass introduction of multimedia interactive terminal, available on smartphones

Summary

Introduction

For machine-supported detection of segments of interest in medical images and understanding the content with learning algorithms [1] and [2], neural networks [3] and [4], naive Bayes, support vector machine (SVM) and others [5] are among key diagnostic tools of modern medicine. This article represents a new effort to establish machine-supported analysis of medical images related to erythema migrans, an early manifestation of Lyme borreliosis. Lyme borreliosis is a multisystem disease [7], caused by Borrelia burgdorferi sensu lato and transmitted by a tick bite. The early course of Lyme borreliosis is characterized by an expanding skin lesion named erythema migrans. Recognition and proper antibiotic treatment can successfully prevent harmful effects of Lyme borreliosis and enable faster disappearance of erythema migrans

Methods

Results

Conclusion