Staff View: Digital Assessment of Facial Acne Vulgaris

Digital Assessment of Facial Acne Vulgaris

Acne affects 85% of adolescents at some time during their lives. Dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. However, these manual methods are time consuming and may result in intra-observer and inter-observer variations, even...

Full description

Saved in:

Bibliographic Details
Main Authors:	Malik, Aamir Saeed, Ramli, Roshaslinie, Hani, Ahmad Fadzil M, Salih, Yasir, Yap, Felix Boon-Bin, Nisar, Humaira
Format:	Conference or Workshop Item
Published:	2014
Subjects:	Q Science (General) T Technology (General)
Online Access:	http://eprints.utp.edu.my/11406/1/Digital%20assessment%20of%20facial%20acne%20vulgaris%20-%20Paper.pdf http://eprints.utp.edu.my/11406/
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utp.eprints.11406
record_format	eprints
spelling	my.utp.eprints.114062015-04-28T02:54:13Z Digital Assessment of Facial Acne Vulgaris Malik, Aamir Saeed Ramli, Roshaslinie Hani, Ahmad Fadzil M Salih, Yasir Yap, Felix Boon-Bin Nisar, Humaira Q Science (General) T Technology (General) Acne affects 85% of adolescents at some time during their lives. Dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. However, these manual methods are time consuming and may result in intra-observer and inter-observer variations, even by experienced dermatologists. The objective of this research is to develop a computational imaging method for automated acne grading. The first step in the proposed method is pre-processing which involves lighting compensation. The CIE Lab color space is used to measure any dissimilarity between skin colors. Acne segmentation has been performed using automated modified K-means clustering algorithm and support vector machines (SVM) classifier. Color and diameter are the main features extracted to classify acne blobs into different acne classes; papule, pustule, nodule or cyst. Finally, the severity level is determined such as mild, moderate, severe and very severe. Keywords-K-means clustering, SVM Classifier, Feature Extraction, Acne Grading System 2014 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/11406/1/Digital%20assessment%20of%20facial%20acne%20vulgaris%20-%20Paper.pdf Malik, Aamir Saeed and Ramli, Roshaslinie and Hani, Ahmad Fadzil M and Salih, Yasir and Yap, Felix Boon-Bin and Nisar, Humaira (2014) Digital Assessment of Facial Acne Vulgaris. In: 2014 IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement for Sustainable Development, I2MTC 2014. http://eprints.utp.edu.my/11406/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
topic	Q Science (General) T Technology (General)
spellingShingle	Q Science (General) T Technology (General) Malik, Aamir Saeed Ramli, Roshaslinie Hani, Ahmad Fadzil M Salih, Yasir Yap, Felix Boon-Bin Nisar, Humaira Digital Assessment of Facial Acne Vulgaris
description	Acne affects 85% of adolescents at some time during their lives. Dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. However, these manual methods are time consuming and may result in intra-observer and inter-observer variations, even by experienced dermatologists. The objective of this research is to develop a computational imaging method for automated acne grading. The first step in the proposed method is pre-processing which involves lighting compensation. The CIE Lab color space is used to measure any dissimilarity between skin colors. Acne segmentation has been performed using automated modified K-means clustering algorithm and support vector machines (SVM) classifier. Color and diameter are the main features extracted to classify acne blobs into different acne classes; papule, pustule, nodule or cyst. Finally, the severity level is determined such as mild, moderate, severe and very severe. Keywords-K-means clustering, SVM Classifier, Feature Extraction, Acne Grading System
format	Conference or Workshop Item
author	Malik, Aamir Saeed Ramli, Roshaslinie Hani, Ahmad Fadzil M Salih, Yasir Yap, Felix Boon-Bin Nisar, Humaira
author_facet	Malik, Aamir Saeed Ramli, Roshaslinie Hani, Ahmad Fadzil M Salih, Yasir Yap, Felix Boon-Bin Nisar, Humaira
author_sort	Malik, Aamir Saeed
title	Digital Assessment of Facial Acne Vulgaris
title_short	Digital Assessment of Facial Acne Vulgaris
title_full	Digital Assessment of Facial Acne Vulgaris
title_fullStr	Digital Assessment of Facial Acne Vulgaris
title_full_unstemmed	Digital Assessment of Facial Acne Vulgaris
title_sort	digital assessment of facial acne vulgaris
publishDate	2014
url	http://eprints.utp.edu.my/11406/1/Digital%20assessment%20of%20facial%20acne%20vulgaris%20-%20Paper.pdf http://eprints.utp.edu.my/11406/
_version_	1738655949293355008
score	13.211869

Digital Assessment of Facial Acne Vulgaris

Similar Items