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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >Recurrent neural network based retinal nerve fiber layer defect detection in early glaucoma
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Recurrent neural network based retinal nerve fiber layer defect detection in early glaucoma

机译：基于循环神经网络的青光眼早期视网膜神经纤维层缺损检测

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摘要

Retinal nerve fiber layer defect (RNFLD) is the earliest objective evidence of glaucoma in fundus images. Glaucoma is an optic neuropathy which causes irreversible vision impairment. Early glaucoma detection and its prevention are the only way to prevent further damage to human vision. In this paper, we propose a new automated method for RNFLD detection in fundus images through patch features driven recurrent neural network (RNN). A new dataset of fundus images is created for evaluation purpose which contains several challenging RNFLD boundaries. The true boundary pixels are classified using the RNN trained by novel cumulative zero count local binary pattern (CZC-LBP), directional differential energy (DDE) patch features. The experimental results demonstrate high RNFLD detection rate along with accurate boundary localization.
机译：视网膜神经纤维层缺陷（RNFLD）是眼底图像中青光眼的最早目标证据。青光眼是一种视神经病变，导致不可逆的视力损伤。早期的青光眼检测及其预防是防止人类视力进一步损害的唯一方法。在本文中，我们提出了一种新的自动化方法，通过修补程序通过驱动的经常性神经网络（RNN）在眼底图像中进行RNFLD检测的自动化方法。为评估目的创建了一个新的基底图像数据集，其中包含几个挑战的RNFLD边界。使用由新型累积零计数局部二进制模式（CZC-LBP），定向差分能量（DDE）贴片特征的RNN分类真正的边界像素。实验结果表明了高RNFLD检测率以及精确的边界定位。

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