%0 Journal Article
%@ 0909752X
%A Malik, A.S.
%A Humayun, J.
%A Kamel, N.
%A Yap, F.B.-B.
%D 2014
%F scholars:5185
%I Blackwell Publishing Ltd
%J Skin Research and Technology
%K Algorithms; Automation; Grading; Image enhancement; Image processing; Image segmentation; Pixels, Acne lesions; Acne vulgaris; Automated diagnosis system; Contrast improvements; Enhancement algorithms; High dynamic range images; Image processing - methods; Sensitivity and specificity, Dermatology, acne vulgaris; algorithm; article; color; contrast enhancement; controlled study; histogram; human; image enhancement; image processing; information processing; medical photography; sensitivity and specificity; acne vulgaris; algorithm; automated pattern recognition; computer assisted diagnosis; epiluminescence microscopy; pathology; photography; procedures; reproducibility; skin, Acne Vulgaris; Algorithms; Dermoscopy; Humans; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Photography; Reproducibility of Results; Sensitivity and Specificity; Skin
%N 3
%P 322-331
%R 10.1111/srt.12122
%T Novel techniques for enhancement and segmentation of acne vulgaris lesions
%U https://khub.utp.edu.my/scholars/5185/
%V 20
%X Background: More than 99 acne patients suffer from acne vulgaris. While diagnosing the severity of acne vulgaris lesions, dermatologists have observed inter-rater and intra-rater variability in diagnosis results. This is because during assessment, identifying lesion types and their counting is a tedious job for dermatologists. To make the assessment job objective and easier for dermatologists, an automated system based on image processing methods is proposed in this study. Objectives: There are two main objectives: (i) to develop an algorithm for the enhancement of various acne vulgaris lesions; and (ii) to develop a method for the segmentation of enhanced acne vulgaris lesions. Methods: For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. Then, segmentation is performed by clustering the pixels based on Mahalanobis distance of each pixel from spectral models of acne vulgaris lesions. Results: Two metrics are used to evaluate the enhancement of acne vulgaris lesions, i.e., contrast improvement factor (CIF) and image contrast normalization (ICN). The proposed algorithm is compared with two other methods. The proposed enhancement algorithm shows better result than both the other methods based on CIF and ICN. In addition, sensitivity and specificity are calculated for the segmentation results. The proposed segmentation method shows higher sensitivity and specificity than other methods. Conclusion: This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
%Z cited By 8