Automatic acne detection for medical treatment

Abstract

In this paper, an effective image processing technique has been studied to develop a system of acne detection. The focus is on binary thresholding applied to facial images with various types, shapes or amounts of acne. A typical image on a cheek has been usually used to take in the experiment which results are markings on acnes automatically, this method is more effective than manual counting by a typical dermatologist. An input image is first taken into gray and special color model from regular red-green-blue version, this work is for comparison and revelation of region of interest. The regions of interest are mostly acnes but it can contain some noise. Then binary threshold has been used to clearly show and can remove the noise easier from regions of acne. A box shape is constructed to mark the final result of acne detection, so that correct and incorrect detected acne can be displayed and calculated for a system efficiency. Experimental output can be summarized that accuracy, precision and sensitivity are quite fluctuated depending on color, shape and lighting condition of acnes in the image.

Automatic facial flaw detection and retouching via discriminative structure tensor

“…We first perform our proposed flaw detection approach on various facial examples, and select to compare the detection performance with four competitive works, i.e. determinant of Hessian (DOH) method [29], Chang and Liao [30], Chantharaphaichi et al [31] and saliency model [14]. Typical examples were shown in Fig.…”

“…Similarly, Chang and Liao [30] held a strong ability to detect some significant flaws, but which also failed to detect some lighted‐coloured spots due to its limited texture characterisation power. Although Chantharaphaichi et al [31] has designed a very simple imperfection detection method, its detection performance was also a bit poor in practice. The main reason lies that this approach just utilised the colour information and binary thresholding to mark the flaws in a global way, which often degraded its detection performance under uneven appearances.…”

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).…”

AcneNet - A Deep CNN Based Classification Approach for Acne Classes

“…Also, a long training period is required in this method. To overcome this problem a variety of image processing and machine learning techniques are recently used to accurately identify the skin problems arising from acne in the human face [9]. VISIA is a widely used commercial instrument which aims to identify the acne types in the skin during cosmetic surgery.…”