Srivastava Ruchir, Gao Xinting, Yin Fengshou, Wong Damon W K, Liu Jiang, Cheung Carol Y, Wong Tien Yin
Institute for Infocomm Research , 138632 Singapore.
Singapore Eye Research Institute , Singapore 168751, Singapore.
J Med Imaging (Bellingham). 2014 Apr;1(1):014502. doi: 10.1117/1.JMI.1.1.014502. Epub 2014 Jun 4.
This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.
本文旨在通过裂隙灯图像对核性白内障（NC）进行自动分级，以减少传统手动分级的工作量。关于该主题的现有工作大多使用晶状体的亮度和颜色来完成任务，而未考虑晶状体各部分的可见性。本文的主要贡献在于通过提出基于灰度图像梯度的特征来利用可见性线索，以实现核性白内障的自动分级。梯度对于该任务很重要，因为在健康眼睛中，晶状体各部分的清晰可见性会导致晶状体区域出现明显的边缘，但随着白内障严重程度的增加，这些边缘会逐渐模糊。在一个包含5000多张裂隙灯图像的大型数据集上进行的实验表明，所提出的特征在速度和准确性方面均优于现有最佳特征。此外，将所提出的特征与先前的特征进行融合，其结果比单独使用这两种特征中的任何一种都要好。
