Novel techniques for enhancement and segmentation of acne vulgaris lesions

Abstract

This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions.

Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs

“…[15,24] Other work has already been done to develop an automatic classification system of acne patients, focusing on the automatization of acne lesion counts or for the severity measurement using the IGA scale, widely used in the USA. [8][9][10] However, to our knowledge, this is the first time that an AIA for smartphones has been developed that assesses acne severity based on the GEA scale and that allows the identification and counting of acne lesions with reliability comparable to that of a trained dermatologist. Moreover, the present algorithm may be suitable for the three main racial groups, suggesting a high generalizability.…”

“…However, to date, these methods have still not been validated. [8][9][10] A recent study conducted in the USA assessed the severity of acne using Artificial Intelligence (AI) and the IGA scale. [8] AI was able to classify acne lesions according to an IGA ordinal scale with high accuracy, no human intervention and with no need for lesion counting.…”

Prioritizing treatment outcomes: How people with acne vulgaris decide if their treatment is working

“…These changes cannot be easily captured by existing physician administered grading scales or by assessments made at fixed points in time, as in a clinical trial. While standardized digital imaging techniques now under development may provide a potential novel and improved method to capture more subtle treatment effects, the findings suggest that patient‐reported symptoms and signs should be considered within future clinical trials.…”

Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images

“…To conduct a systematic review on acne images segmentation methods, an adapted PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) (Liberati et al, 2009) standard was used. The analysis includes all studies published until April 2019 and explores four databases: Scopus (https://www.scopus.com/home.uri), PubMed Central The review shows that current segmentation methods for acne vulgaris images can be divided into two groups: those algorithms based on classical image processing techniques (Ramli, Malik, Hani, & Yap, 2011a;Chen, Chang, & Cao, 2012;Khongsuwan, Kiattisin, Wongseree, & Leelasantitham, 2012;Humayun, Malik, Belhaouari, Kamel, & Yap, 2012;Liu & Zerubia, 2013;Min, Kong, Yoon, Kim, & Suh, 2013;Malik, Humayun, Kamel, & Yap, 2014;Chantharaphaichi, Uyyanonvara, Sinthanayothin, & Nishihara, 2015;Alamdari, Tavakolian, Alhashim, & Fazel-Rezai, 2016;Kittigul & Uyyanonvara, 2016;Budhi, Adipranata, & Gunawan, 2017;Maroni, Ermidoro, Previdi, & Bigini, 2017) -they consist of a series of steps or operations that have to be applied to an image, for instance color space transformations or contrast modifications. The other group refers to machine learning algorithms (Fujii et al, 2008;Ramli, Malik, Hani, & Yap, 2011b;Madan, Dana, & Cula, 2011;Arifin, Kibria, Firoze, Amini, & Yan, 2012;Chang & Liao, 2013;Khan, Malik, Kamel, Dass, & Affandi, 2015;Alamdari et al, 2016).…”