Department of Electric and Electronic Engineering, University of Melbourne, VIC, Australia.
IBM Research - Australia, Department of Electrical and Electronic Engineering, University of Melbourne, VIC, Australia.
Comput Med Imaging Graph. 2018 Jun;66:44-55. doi: 10.1016/j.compmedimag.2018.02.004. Epub 2018 Feb 23.
Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively.
银屑病是一种慢性皮肤病，可能危及生命。准确的严重程度评分有助于皮肤科医生决定治疗方案。本文提出了一种用于银屑病图像自动红斑严重程度评分的半监督计算机辅助系统。首先，无监督阶段包括一种新颖的图像表示方法。我们构建了一个字典，然后在稀疏表示中用于局部特征提取。为了获得最终的图像表示向量，利用局部特征进行聚合方法。其次，监督阶段是训练各种多类机器学习（ML）分类器进行红斑严重程度评分。最后，我们将所提出的系统与两种流行的无监督特征提取方法，即：视觉词汇袋模型（BoVWs）和 AlexNet 预训练模型进行比较。均方根误差（RMSE）和 F1 分数分别用作学习字典和训练的 ML 模型的性能度量。本研究使用了一个包含 676 张图像的银屑病图像集。实验结果表明，使用所提出的程序可以提供一种设置，其中红斑评分准确且一致。此外，研究还表明，具有大量原子和小补丁大小的字典可以产生最佳的代表性红斑严重程度特征。进一步，随机森林（RF）的 F1 得分 0.71 优于其他分类器，其次是支持向量机（SVM）和提升，分别为 0.66 和 0.64。此外，进行的比较研究证实了所提出方法的有效性，与基于 BoVWs 和 AlexNet 的特征相比，分别提高了 9%和 12%。
