Department of Electronics and Communication Engineering, HKBK College of Engineering, India.
Department of Computer Science and Engineering, Sona College of Technology, India.
Biomed Res Int. 2022 May 2;2022:6799184. doi: 10.1155/2022/6799184. eCollection 2022.
Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup-to-disc ratio is considered essential in diagnosing glaucoma. It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio. In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). The mRNN use the combination of recurrent neural network (RNN) with fully convolutional network (FCN) that exploits the intra- and interslice contexts. The FCN extracts the contents from an input image by constructing a feature map for the intra- and interslice contexts. This is carried out to extract the relevant information, where RNN concentrates more on interslice context. The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc. The results of simulation show that the proposed method mRNN is efficient in improving the rate of segmentation than the other deep learning models like Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.
青光眼是导致视力丧失的主要因素之一，患者的视力往往会迅速下降。杯盘比的检查被认为是诊断青光眼的重要手段。因此，视盘和杯的分割对于寻找该比例是有用的。在本文中，我们使用改进的递归神经网络（mRNN）从输入的眼部图像中开发了视盘和杯的提取和分割。mRNN 结合了递归神经网络（RNN）和全卷积网络（FCN），利用了切片内和切片间的上下文。FCN 通过构建用于切片内和切片间上下文的特征图，从输入图像中提取内容。这是为了提取相关信息，其中 RNN 更专注于切片间的上下文。进行了模拟以测试该模型整合上下文信息以实现最佳光学杯和盘分割的功效。模拟结果表明，与 Drive、STARE、MESSIDOR、ORIGA 和 DIARETDB 等其他深度学习模型相比，所提出的方法 mRNN 可有效提高分割率。
