%P 546-550
%A A.S. Malik
%A R. Ramli
%A A.F.M. Hani
%A Y. Salih
%A F.B.-B. Yap
%A H. Nisar
%I Institute of Electrical and Electronics Engineers Inc.
%T Digital assessment of facial acne vulgaris
%C Montevideo
%J Conference Record - IEEE Instrumentation and Measurement Technology Conference
%L scholars5001
%O cited By 14; Conference of 2014 IEEE International Instrumentation and Measurement Technology Conference: Instrumentation and Measurement for Sustainable Development, I2MTC 2014 ; Conference Date: 12 May 2014 Through 15 May 2014; Conference Code:106826
%R 10.1109/I2MTC.2014.6860804
%D 2014
%K Color; Color photography; Dermatology; Feature extraction; Planning; Support vector machines; Sustainable development, Computational imaging; Flash photography; Grading system; K-means clustering; Lighting compensation; Modified k-means clustering; SVM classifiers; Visual assessments, Grading
%X 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 La b* 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. © 2014 IEEE.