Acne Segmentation and Classification using Region Growing and Self-Organizing Map

Budhi, Gregorius Satia; Adipranata, Rudy; Gunawan, Ari

doi:10.1109/icsiit.2017.62

Cited by 11 publications

(7 citation statements)

References 5 publications

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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%

“…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.…”

Section: Systematic Reviewmentioning

confidence: 99%

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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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Section: Systematic Reviewmentioning

confidence: 99%

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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show abstract

“…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 .…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Perís-Fajarnés

Santonja

García

et al. 2020

Skin Research and Technology

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Acne vulgaris is an inflammatory chronic disease of pilosebaceous units-a pilosebaceous unit is formed by all the hair follicles related to the same sebaceous gland. The main affected regions are face, neck, chest and back. Clinical manifestations are seborrhoea or overproduction of sebum, the presence of non-inflammatory and inflammatory lesions on skin (open and closed commedos, and pustules and papules, respectively), and scars. 1 It is the most common dermatological pathology worldwide. 2 85% of adolescents suffer from acne. 3 In order to monitor and treat acne properly, a precise and reliable method to establish acne severity is needed. 4 Currently, there is a wide range of acne grading systems, which shows the lack of a global standard. These systems can be divided into two groups: those based on manual lesion counting and the ones that use a model photography. 3,4 Several studies have proven lesion counting to be an objective and reliable method. 4,5

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“…The main motive behind the research is to find a proper computational imaging method for automatic detection of acne using images that are taken by cell phone and then the classification of the different type of acne lesions from each other. [1,2].…”

Section: Introductionmentioning

confidence: 99%

Online Prescription for Skin Diseases

Rajurkar¹,

Kodwani²,

Singh³

et al. 2019

IJATCSE

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Acne vulgaris is a common skin disease that is found in all humans. There are various types of acnes according to their severity. In this research, an application was developed to segment and find out the acne on patients image. In this paper an acne detection method based on image processing techniques were processed. It is mobile based and hence very accessible even in remote areas and it is completely noninvasive to patient's skin. An image of the infected area of the skin is provided as an input to prototype by patient. Different Image processing techniques are performed on this image and corresponding disease detected is displayed at the output. This proposed system is highly beneficial in rural areas where access to dermatologists is limited.

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Acne Segmentation and Classification using Region Growing and Self-Organizing Map

Cited by 11 publications

References 5 publications

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

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

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

Online Prescription for Skin Diseases

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