Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
J Healthc Eng. 2021 Aug 31;2021:3561134. doi: 10.1155/2021/3561134. eCollection 2021.
We present in this paper a novel optic disc detection method based on a fully convolutional network and visual saliency in retinal fundus images. Firstly, we employ the morphological reconstruction-based object detection method to locate the optic disc region roughly. According to the location result, a 400 × 400 image patch that covers the whole optic disc is obtained by cropping the original retinal fundus image. Secondly, the Simple Linear Iterative Cluster approach is utilized to segment such an image patch into many smaller superpixels. Thirdly, each superpixel is assigned a uniform initial saliency value according to the background prior information based on the assumption that the superpixels located on the boundary of the image belong to the background. Meanwhile, we use a pretrained fully convolutional network to extract the deep features from different layers of the network and design the strategy to represent each superpixel by the deep features. Finally, both the background prior information and the deep features are integrated into the single-layer cellular automata framework to gain the accurate optic disc detection result. We utilize the DRISHTI-GS dataset and RIM-ONE r3 dataset to evaluate the performance of our method. The experimental results demonstrate that the proposed method can overcome the influence of intensity inhomogeneity, weak contrast, and the complex surroundings of the optic disc effectively and has superior performance in terms of accuracy and robustness.
我们在本文中提出了一种基于全卷积网络和视网膜眼底图像视觉显著度的新型视盘检测方法。首先，我们采用基于形态重建的目标检测方法粗略定位视盘区域。根据定位结果，通过裁剪原始眼底图像获得一个包含整个视盘的 400×400 图像块。其次，采用简单线性迭代聚类方法将该图像块分割成许多较小的超像素。然后，根据边界背景先验信息，为每个超像素分配均匀的初始显著度值，假设图像边界上的超像素属于背景。同时，我们利用预先训练好的全卷积网络从网络的不同层提取深度特征，并设计策略用深度特征表示每个超像素。最后，将背景先验信息和深度特征集成到单层元胞自动机框架中，以获得准确的视盘检测结果。我们利用 DRISHTI-GS 数据集和 RIM-ONE r3 数据集来评估我们方法的性能。实验结果表明，所提出的方法能够有效地克服视盘强度不均匀、对比度弱和复杂环境的影响，在准确性和鲁棒性方面具有优越的性能。
