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首页> 外文期刊>Biocybernetics and biomedical engineering >Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images
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Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images

机译：青光眼检测的临床和多分辨率特征的组合及其使用眼底图像的分类

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Glaucoma is a neuro-degenerative disorder of the eye and it leads to permanent blindness when untreated or detected in the later stage. The main cause of glaucoma is the damage of the optic nerve, which occurs due to the increase of eye pressure. Hence the early detection of this disease is critical in time and which can help to prevent further vision loss. The assessment of optic nerve head using fundus images is more beneficial than the raised intra ocular pressure assessment in population-based glaucoma screening. This work proposed a novel method for glaucoma identification based on time-invariant feature cup to disk ratio and anisotropic dual-tree complex wavelet transform features. Optic disk segmentation is done by using Fuzzy C-Means clustering method and Otsu's thresholding is used for optic cup segmentation. The results show the proposed method achieved an accuracy rate of 97.67% with 98% sensitivity using a multilayer perceptron model that is considered as clinically significant when compared to the existing works. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
机译：青光眼是眼睛的神经退行性疾病，当在后期未经处理或检测到时，它导致永久性失明。青光眼的主要原因是视神经的损害，由于眼压的增加而发生。因此，这种疾病的早期检测及时是至关重要的，这有助于防止进一步的视力丧失。使用眼底图像评估视神经头部比基于人群的青光眼筛选中提高的眼压评估更有益。这项工作提出了一种基于时间不变特征杯的青光眼识别的新方法，以及磁盘比和各向异性双树复杂小波变换特征。光盘分割是通过使用模糊C-means聚类方法完成的，并且OTSU的阈值用于光学杯分割。结果表明，使用多层的Perceptron模型，所提出的方法实现了97.67％的精度率为97.67％，与现有工程相比，在临床上视为临床意义。 （c）2018年纳雷斯州博士生物庭院研究所和波兰科学院的生物医学工程。 elsevier b.v出版。保留所有权利。

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