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Measuring Surface Area of Skin Lesions with 2D and 3D Algorithms

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Purpose The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In… Click to show full abstract

Purpose The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In this paper, we present a new approach to measure the skin lesion surface in two and three dimensions. Methods For the 2D approach, a single photo containing a flexible paper ruler is taken. After semi-automatic segmentation of the lesion, evaluation is based on local scale estimation using the ruler. For the 3D approach, reconstruction is based on Structure from Motion. Roughly outlining the region of interest around the lesion is required for both methods. Results The measurement evaluation was performed on 117 phantom images and five phantom videos for 2D and 3D approach, respectively. We found an absolute error of 0.99±1.18  cm2 and a relative error 9.89± 9.31% for 2D. These errors are <1  cm2 and <5% for five test phantoms in our 3D case. As expected, the error of 2D surface area measurement increased by approximately 10% for wounds on the bent surface compared to wounds on the flat surface. Using our method, the only user interaction is to roughly outline the region of interest around the lesion. Conclusions We developed a new wound segmentation and surface area measurement technique for skin lesions even on a bent surface. The 2D technique provides the user with a fast, user-friendly segmentation and measurement tool with reasonable accuracy for home care assessment of treatment. For 3D only preliminary results could be provided. Measurements were only based on phantoms and have to be repeated with real clinical data.

Keywords: skin lesions; surface area; surface; measuring surface; lesion

Journal Title: International Journal of Biomedical Imaging
Year Published: 2019

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