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Journal of Medical Imaging

Automatic psoriasis lesion segmentation in two-dimensional skin images using multiscale superpixel clustering
Author(s): Yasmeen M. George; Mohammad Aldeen; Rahil Garnavi
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Paper Abstract

Psoriasis is a chronic skin disease that is assessed visually by dermatologists. The Psoriasis Area and Severity Index (PASI) is the current gold standard used to measure lesion severity by evaluating four parameters, namely, area, erythema, scaliness, and thickness. In this context, psoriasis skin lesion segmentation is required as the basis for PASI scoring. An automatic lesion segmentation method by leveraging multiscale superpixels and K -means clustering is outlined. Specifically, we apply a superpixel segmentation strategy on CIE- L * a * b * color space using different scales. Also, we suppress the superpixels that belong to nonskin areas. Once similar regions on different scales are obtained, the K -means algorithm is used to cluster each superpixel scale separately into normal and lesion skin areas. Features from both a * and b * color bands are used in the clustering process. Furthermore, majority voting is performed to fuse the segmentation results from different scales to obtain the final output. The proposed method is extensively evaluated on a set of 457 psoriasis digital images, acquired from the Royal Melbourne Hospital, Melbourne, Australia. Experimental results have shown evidence that the method is very effective and efficient, even when applied to images containing hairy skin and diverse lesion size, shape, and severity. It has also been ascertained that CIE- L * a * b * outperforms other color spaces for psoriasis lesion analysis and segmentation. In addition, we use three evaluation metrics, namely, Dice coefficient, Jaccard index, and

Paper Details

Date Published: 10 November 2017
PDF: 14 pages
J. Med. Imag. 4(4) 044004 doi: 10.1117/1.JMI.4.4.044004
Published in: Journal of Medical Imaging Volume 4, Issue 4
Show Author Affiliations
Yasmeen M. George, The Univ. of Melbourne (Australia)
Mohammad Aldeen, The Univ. of Melbourne (Australia)
Rahil Garnavi, Univ. of Melbourne (Australia)
IBM Research-Australia (Australia)


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