NDLI: Digital assessment of facial acne vulgaris

Content Provider	IEEE Xplore Digital Library
Author	Malik, A.S. Ramli, R. Hani, A.F.M. Salih, Y. Yap, F.B.-B. Nisar, H.
Copyright Year	2014
Description	Author affiliation: Dept. of Dermatology, Hosp. Kuala Lumpur, Kuala Lumpur, Malaysia (Yap, F.B.-B.) \|\| Dept. of Electr. & Electron. Eng., Univ. Teknol. Petronas, Tronoh, Malaysia (Malik, A.S.; Ramli, R.; Hani, A.F.M.; Salih, Y.) \|\| Fac. of Eng. & Green Technol., Univ. Tunku Abdul Rahman, Kampar, Malaysia (Nisar, H.)
Abstract	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.
Starting Page	546
Ending Page	550
File Size	1104403
Page Count	5
File Format	PDF
ISBN	9781467363860
DOI	10.1109/I2MTC.2014.6860804
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-12
Publisher Place	Uruguay
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Skin Lesions Image color analysis Feature extraction Image segmentation Lighting Support vector machines Acne Grading System K-means clustering SVM Classifier Feature Extraction
Content Type	Text
Resource Type	Article

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