Localization of acne lesion through template matching

Abstract

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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).…”

“…An analysis of limitations for each study included in the systematic review showed that algorithms based on classical image processing techniques cannot be totally automatized, mainly because there are some parameters that need to be manually adjusted (Son et al, 2008;Humayun et al, 2012;Budhi et al, 2017;Maroni et al, 2017). That is why in the present work machine learning algorithms are chosen for the implementation of the proposed methodology.…”

Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images

“…A total of 20 studies were found. The review showed that current segmentation methods for acne vulgaris images can be divided into two groups: those algorithms based on classical image processing techniques 10‐22 —they consist of a series of steps or operations that have to be applied to an image, for instance colour space transformations or contrast modifications. The other group refers to machine learning algorithms 18,23‐29 .…”

“…The existing methodologies for acne fluorescence images are both based on classical image processing techniques. However, from the analysis of limitations declared on each analysed study, it can be concluded that algorithms based on these techniques cannot be totally automatized, mainly because there are some parameters that need to be manually adjusted 13,20‐22 . That is why in the present work machine learning algorithms are chosen for the implementation of the proposed methodology.…”

Automatic acne detection for medical treatment

“…This approach proved to be affective for counting and recognizing of lesions by letting a system learn the appearance of acne in advance to do matching. [10] Cula employed multispectral imaging and linear discriminant functions with a time varying component into acne detection and counting system. Gaussian mixture models have been used to detect and register facial acnes over multiple time points, so there must be an alignment technique to synchronize pictures from several times.…”