Centre for Visual Information Technology, International Institute of Information Technology Hyderabad, Hyderabad 500032, India.
IEEE Trans Biomed Eng. 2012 Jun;59(6):1523-31. doi: 10.1109/TBME.2012.2187293. Epub 2012 Feb 10.
Accurate segmentation of the cup region from retinal images is needed to derive relevant measurements for glaucoma assessment. A novel, depth discontinuity (in the retinal surface)-based approach to estimate the cup boundary is proposed in this paper. The proposed approach shifts focus from the cup region used by existing approaches to cup boundary. The given set of images, acquired sequentially, are related via a relative motion model and the depth discontinuity at the cup boundary is determined from cues such as motion boundary and partial occlusion. The information encoded by these cues is used to approximate the cup boundary with a set of best-fitting circles. The final boundary is found by considering points on these circles at different sectors using a confidence measure. Four different kinds of data sets ranging from synthetic to real image pairs, covering different multiview scenarios, have been used to evaluate the proposed method. The proposed method was found to yield an error reduction of 16% for cup-to-disk vertical diameter ratio (CDR) and 13% for cup-to-disk area ratio (CAR) estimation, over an existing monocular image-based cup segmentation method. The error reduction increased to 33% in CDR and 18% in CAR with the addition of a third view (image) which indicates the potential of the proposed approach.
准确地从视网膜图像中分割出杯区，以便对青光眼进行相关测量。本文提出了一种新的基于深度不连续（在视网膜表面）的方法来估计杯边界。该方法将关注点从现有方法中使用的杯区转移到杯边界。所给的一组图像是通过相对运动模型获得的，并且杯边界的深度不连续性可以从运动边界和部分遮挡等线索中确定。这些线索所编码的信息用于通过一组最佳拟合的圆来近似杯边界。最终的边界是通过在不同扇区考虑这些圆上的点并使用置信度度量来找到的。已经使用了从合成到真实图像对的四种不同类型的数据集，涵盖了不同的多视图场景，以评估所提出的方法。结果表明，与现有的基于单眼图像的杯分割方法相比，该方法在杯盘垂直直径比（CDR）和杯盘面积比（CAR）估计方面的误差减少了 16%和 13%。通过增加第三张（图像）视图，CDR 和 CAR 的误差减少分别增加到 33%和 18%，这表明了该方法的潜力。
