TY  - CONF
SN  - 10915281
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2014///
EP  - 550
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905717229&doi=10.1109%2fI2MTC.2014.6860804&partnerID=40&md5=a0c1a386a45c07a6503f749b888e2a9a
A1  - Malik, A.S.
A1  - Ramli, R.
A1  - Hani, A.F.M.
A1  - Salih, Y.
A1  - Yap, F.B.-B.
A1  - Nisar, H.
AV  - none
CY  - Montevideo
KW  - Color; Color photography; Dermatology; Feature extraction; Planning; Support vector machines; Sustainable development
KW  -  Computational imaging; Flash photography; Grading system; K-means clustering; Lighting compensation; Modified k-means clustering; SVM classifiers; Visual assessments
KW  -  Grading
SP  - 546
TI  - Digital assessment of facial acne vulgaris
ID  - scholars5001
N2  - 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.
N1  - 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
ER  -