Counting number of points for acne vulgaris using UV Fluorescence and image processing

Khongsuwan, Manita; Kiattisin, Supaporn; Wongseree, Waranyu; Leelasantitham, Adisorn

doi:10.1109/bmeicon.2012.6172038

Cited by 15 publications

(19 citation statements)

References 10 publications

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“…[12] The values multiplied in each color are obtained from experiment. RGB image must be processed to gray scale because it is simpler to calculate in program.…”

Section: Methodsmentioning

confidence: 99%

Automatic acne detection for medical treatment

Chantharaphaichi¹,

Uyyanonvara²,

Sinthanayothin

et al. 2015

2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

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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.

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“…[12] The values multiplied in each color are obtained from experiment. RGB image must be processed to gray scale because it is simpler to calculate in program.…”

Section: Methodsmentioning

confidence: 99%

Automatic acne detection for medical treatment

Chantharaphaichi¹,

Uyyanonvara²,

Sinthanayothin

et al. 2015

2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

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“…When the ROI image is input, the RGB image is transformed to a gray image, and a twolevel Haar DWT (10) is performed on the gray image, as shown in Fig. 4.…”

Section: Dwtmentioning

confidence: 99%

“…The converted grayscale images are easier to process than the ROI images. (10) The normalization step scales the pixels of grayscale images to adjust the brightness of the image, as shown in Fig. 5.…”

Section: Acne Detectionmentioning

confidence: 99%

System for Recommending Facial Skincare Products

Hsia¹,

Lin²,

Lin³

et al. 2020

Sensors and Materials

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Acne is a skin problem that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars and many individuals must use skincare products or undertake medical treatment. However, the use of inappropriate skincare products can exacerbate skin conditions. We propose the use of multifeature processing and classification of skin quality and acne status by machine learning to make recommendations for facial skincare products. The results for 15 subjects show that the proposed system achieves a consumer satisfaction index of 80%.

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“…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).…”

Section: Systematic Reviewmentioning

confidence: 99%

“…In order to perform segmentation, different image modalities are used, but conventional photographs are the most common modality. Fluorescence images are used in only two studies (Son, Han, Jung, & Nelson, 2008;Khongsuwan et al, 2012).…”

Section: Systematic Reviewmentioning

confidence: 99%

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

Perís-Fajarnés

Santonja

Alamán

et al. 2019

Proceedings INNODOCT/19. International Conference on Innovation, Documentation and Education

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Acne vulgaris is one of the most common human pathologies worldwide. Its prevalence causes a high healthcare expenditure. Acne healthcare costs and effects on individuals' quality of life lead to the need of analysing current acne evaluation, treatment and monitoring methods. One of the most common ones is manual lesion counting by a dermatologist. However, this technique has several limitations, such as time spent. That is the reason why the development of new computer-assisted techniques are needed in order to automatically count the acne lesions. Nonetheless, the first step is automatic acne lesion detection on the skin of patients. The aim of this work is to propose a new methodology to solve the acne images segmentation problem, so that the implementation of a system for automatic counting is possible. The results would be a decrease in both time spent and diagnosis errors. With this objective, after doing a systematic review on the state of the art of acne images segmentation methods, fluorescence images of the face of acne patients are obtained. This image modality enhances visualization of the acne lesions. Finally, using the fluorescence images, a segmentation algorithm is implemented in MATLAB.

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Counting number of points for acne vulgaris using UV Fluorescence and image processing

Cited by 15 publications

References 10 publications

Automatic acne detection for medical treatment

Automatic acne detection for medical treatment

System for Recommending Facial Skincare Products

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

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