Cheng Jun, Zhang Zhuo, Tao Dacheng, Wong Damon Wing Kee, Liu Jiang, Baskaran Mani, Aung Tin, Wong Tien Yin
Institute for Infocomm Research, ASTAR, Singapore.
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Biomed Opt Express. 2017 Jul 20;8(8):3763-3777. doi: 10.1364/BOE.8.003763. eCollection 2017 Aug 1.
Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well.
从彩色眼底图像中自动计算杯盘比（CDR）已被证明在青光眼检测方面具有前景。在过去十年中，已经提出了许多算法。在本文中，我们首先回顾该领域的近期工作，然后提出一种用于自动CDR估计的新颖的相似性正则化稀疏组套索方法。所提出的方法通过将测试盘图像与参考盘图像之间的相似性与稀疏组套索约束相结合，基于一组参考盘图像重建测试盘图像。然后使用重建系数来估计测试图像的CDR。所提出的方法已使用650张具有手动标注CDR的图像进行了验证。实验结果表明，平均CDR误差为0.0616，相关系数为0.7，优于其他方法。当分别使用手动分割和自动分割的视盘时，诊断测试中的曲线下面积分别达到0.843和0.837，也优于其他方法。
