Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1328-1331. doi: 10.1109/EMBC.2016.7590952.
Automated glaucoma detection is an important application of retinal image analysis. Compared with segmentation based approaches, image classification based approaches have a potential of better performance. However, it still remains a challenging problem for two reasons. Firstly, due to insufficient sample size, learning effective features is difficult. Secondly, the shape variations of optic disc introduce misalignment. To address these problem, a new classification based approach for glaucoma detection is proposed, in which deep convolutional networks derived from large-scale generic dataset is used to representing the visual appearance and holistic and local features are combined to mitigate the influence of misalignment. The proposed method achieves an area under the receiver operating characteristic curve of 0.8384 on the Origa dataset, which clearly demonstrates its effectiveness.
自动青光眼检测是视网膜图像分析的一项重要应用。与基于分割的方法相比，基于图像分类的方法具有性能更优的潜力。然而，由于两个原因，它仍然是一个具有挑战性的问题。首先，由于样本量不足，学习有效的特征很困难。其次，视盘的形状变化会导致图像未对齐。为了解决这些问题，提出了一种新的基于分类的青光眼检测方法，该方法使用从大规模通用数据集中导出的深度卷积网络来表示视觉外观，并结合整体和局部特征来减轻未对齐的影响。所提出的方法在Origa数据集上的受试者工作特征曲线下面积达到了0.8384，这清楚地证明了其有效性。
