Atopic Dermatitis (AD), a chronic inflammatory skin disease causing lesions, often causes decreased quality of life (Kapur, 2018). Segmentation, a method of illustrating the difference between lesioned and non-lesioned areas of interest (AOIs) has been the primary method for which AD has been studied (Ranteke & Jain, 2013). Manual segmentation is prone to subjectivity (Ning et al., 2014) and automatic segmentation, while reliable and efficient, poses challenges such as light reflections and color variations (Lu et al., 2013). Yet, AD can be classified from color and texture (Hanifin et al., 2001; Nisar et al., 2013), as well as through machine learning methods. The purpose of this study was to determine the optimal method for segmentation of images of atopic dermatitis on subject arms in a novel and standardized photography lightbox (Lightbox) and of images of subjects' self-acquired at-home photos. The goals of this study were to determine the accuracy and reliability of photo acquisition of arms of subjects with AD in a novel standardized photography lightbox, compared to photo acquisition by subjects at home, and determine the accuracy and reliability of automated segmentation of AD lesions with combined color-based segmentation and the U-Net CNN.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43450 |
| Date | 23 November 2021 |
| Creators | London, Matt |
| Contributors | Thomas, Kevin, Kabiri, Nina |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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