Hani Ahmad Fadzil M, Prakasa Esa, Nugroho Hermawan, Affandi Azura M, Hussein Suraiya H
Centre for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Malaysia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4398-401. doi: 10.1109/EMBC.2012.6346941.
Psoriasis is a common skin disorder with a prevalence of 0.6 - 4.8% around the world. The most common is plaques psoriasis and it appears as red scaling plaques. Psoriasis is incurable but treatable in a long term treatment. Although PASI (Psoriasis Area and Severity Index) scoring is recognised as gold standard for psoriasis assessment, this method is still influenced by inter and intra-rater variation. An imaging and analysis system called α-PASI is developed to perform PASI scoring objectively. Percentage of lesion area to the body surface area is one of PASI parameter. In this paper, enhanced imaging methods are developed to improve the determination of body surface area (BSA) and lesion area. BSA determination method has been validated on medical mannequin. BSA accuracies obtained at four body regions are 97.80% (lower limb), 92.41% (trunk), 87.72% (upper limb), and 83.82% (head). By applying fuzzy c-means clustering algorithm, the membership functions of lesions area for PASI area scoring have been determined. Performance of scoring result has been tested with double assessment by α-PASI area algorithm on body region images from 46 patients. Kappa coefficients for α-PASI system are greater than or equal to 0.72 for all body regions (Head - 0.76, Upper limb - 0.81, Trunk - 0.85, Lower limb - 0.72). The overall kappa coefficient for the α-PASI area is 0.80 that can be categorised as substantial agreement. This shows that the α-PASI area system has a high reliability and can be used in psoriasis area assessment.
银屑病是一种常见的皮肤疾病，全球患病率为0.6%-4.8%。最常见的是斑块状银屑病，表现为红色鳞屑斑块。银屑病无法治愈，但通过长期治疗可以得到控制。虽然银屑病面积和严重程度指数（PASI）评分被认为是评估银屑病的金标准，但该方法仍受评分者间和评分者内差异的影响。一种名为α-PASI的成像与分析系统被开发出来，用于客观地进行PASI评分。皮损面积占体表面积的百分比是PASI参数之一。本文开发了增强成像方法，以改进体表面积（BSA）和皮损面积的测定。BSA测定方法已在医学模型上得到验证。在四个身体部位获得的BSA准确率分别为：下肢97.80%、躯干92.41%、上肢87.72%、头部83.82%。通过应用模糊c均值聚类算法，确定了PASI面积评分中皮损面积的隶属函数。通过α-PASI面积算法对46例患者身体部位图像进行双重评估，测试了评分结果的性能。α-PASI系统在所有身体部位的kappa系数均大于或等于0.72（头部-0.76、上肢-0.81、躯干-0.85、下肢-0.72）。α-PASI面积的总体kappa系数为0.80，可归类为高度一致。这表明α-PASI面积系统具有较高的可靠性，可用于银屑病面积评估。
