Automatic psoriasis lesion segmentation in two-dimensional skin images using multiscale superpixel clustering

被引:21
作者
George Y. [1 ]
Aldeen M. [1 ]
Garnavi R. [1 ,2 ]
机构
[1] University of Melbourne, Department of Electrical and Electronic Engineering, VIC
[2] IBM Research, Melbourne, VIC
关键词
K -means clustering; medical image analysis; multiscale superpixels segmentation; psoriasis severity scoring;
D O I
10.1117/1.JMI.4.4.044004
中图分类号
学科分类号
摘要
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 pixel accuracy where scores of 0.783%, 0.698%, and 86.99% have been achieved by the proposed method for the three metrics, respectively. Finally, compared with existing methods that employ either skin decomposition and support vector machine classifier or Euclidean distance in the hue-chrome plane, our multiscale superpixel-based method achieves markedly better performance with at least 20% accuracy enhancement. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
引用
收藏
相关论文
共 31 条
  • [1] International Federation of Psoriasis Associations (IFPA), World Psoriasis Day, (2015)
  • [2] What Is Known about Psoriasis: Statistics, (2015)
  • [3] What Is Psoriasis?, (2015)
  • [4] El Miedany Y., Et al., Using simulation in clinical education: Psoriasis area and severity index (PASI) score assessment, Curr. Rheumatol. Rev., 12, 3, pp. 195-201, (2016)
  • [5] PASI Sore, (1999)
  • [6] Delgado D., Ersboll B., Carstensen J., An image based system to automatically and objectively score the degree of redness and scaling in psoriasis lesions, 13th Danish Conf. on Image Analysis and Pattern Recognition, pp. 130-137, (2004)
  • [7] Shrivastava V.K., Et al., Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind, Comput. Meth. Programs Biomed., 126, pp. 98-109, (2016)
  • [8] Puig L., Et al., Psoriasis beyond the skin: A review of the literature on cardiometabolic and psychological co-morbidities of psoriasis, Eur. J. Dermatol., 24, 3, pp. 305-311, (2014)
  • [9] Zhu H., Et al., Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation, J. Visual Commun. Image Represent., 34, pp. 12-27, (2016)
  • [10] Lu J., Et al., Erythema detection in digital skin images, IEEE Int. Conf. on Image Processing, pp. 2545-2548, (2010)