Glaucoma screening pipeline based on clinical measurements and hidden features
| العنوان: | Glaucoma screening pipeline based on clinical measurements and hidden features |
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| المؤلفون: | Fan Guo, Xin Zhao, Lingzi Jiang, Yuxiang Mai, Beiji Zou, Tang Jin, Xuanchu Duan |
| المصدر: | IET Image Processing. 13:2213-2223 |
| بيانات النشر: | Institution of Engineering and Technology (IET), 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | genetic structures, Computer science, Feature extraction, Glaucoma, 02 engineering and technology, Sliding window protocol, 0202 electrical engineering, electronic engineering, information engineering, medicine, Segmentation, Electrical and Electronic Engineering, Contextual image classification, Artificial neural network, business.industry, 020206 networking & telecommunications, Pattern recognition, Image segmentation, medicine.disease, eye diseases, medicine.anatomical_structure, Signal Processing, 020201 artificial intelligence & image processing, sense organs, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software, Optic disc |
| الوصف: | Glaucoma refers to a chronic disease of the eye that leads to vision loss that is irreversible, which is called ‘silent theft of sight’. Thus, an automatic glaucoma screening pipeline from optic disc (OD) localisation to glaucoma risk prediction is proposed in this study. The proposed pipeline consists of three main phases. Firstly, the OD is localised by morphological processing and sliding window methods. Secondly, a novel neural network which is in U-shape and convolutional introduces concatenating path and fusion loss function is developed to split OD and optic cup (OC) at the same time. Thirdly, both clinical measurements including optic cup-to-disc ratio (CDR), neuroretinal rim related features, and hidden features including statistical moments, entropy and energy are combined to train glaucoma classifiers. According to the results of the experiment, the proposed segmentation network achieves the best performance on both OD and OC segmentation and the proposed CDR calculation method is capable of achieving the performance similar to that of ophthalmologist on CDR measurement. Besides, the authors’ glaucoma classification model can obtain the best performance on sensitivity and area under the curve score in comparison with the existing methods. |
| تدمد: | 1751-9667 1751-9659 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::d34e3cdf46d7ab125aa34e7a4d444611 https://doi.org/10.1049/iet-ipr.2019.0137 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........d34e3cdf46d7ab125aa34e7a4d444611 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17519667 17519659 |
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