کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6920232 1447879 2018 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Psoriasis image representation using patch-based dictionary learning for erythema severity scoring
ترجمه فارسی عنوان
نمایش تصویر پسوریازیازی با استفاده از یادگیری فرهنگ لغت مبتنی بر پچ برای ارزیابی شدت ارادی
کلمات کلیدی
نمره شدت عصب پسوریازیس، سیستم کامپیوتری، یادگیری فرهنگی بدون نظارت، طبقه بندی چند طبقه، استخراج ویژگی مبتنی بر پچ، نمایندگی انحصاری،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computerized Medical Imaging and Graphics - Volume 66, June 2018, Pages 44-55
نویسندگان
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