Institute for Infocomm Research, A*STAR, 138632 Singapore.
IEEE Trans Med Imaging. 2011 Dec;30(12):2126-33. doi: 10.1109/TMI.2011.2164261. Epub 2011 Aug 12.
Under the framework of computer-aided diagnosis, this paper presents an accurate and efficient optic disc (OD) detection and segmentation technique. A circular transformation is designed to capture both the circular shape of the OD and the image variation across the OD boundary simultaneously. For each retinal image pixel, it evaluates the image variation along multiple evenly-oriented radial line segments of specific length. The pixels with the maximum variation along all radial line segments are determined, which can be further exploited to locate both the OD center and the OD boundary accurately. Experiments show that OD detection accuracies of 99.75%, 97.5%, and 98.77% are obtained for the STARE dataset, the ARIA dataset, and the MESSIDOR dataset, respectively, and the OD center error lies around six pixels for the STARE dataset and the ARIA dataset which is much smaller than that of state-of-the-art methods ranging 14-29 pixels. In addition, the OD segmentation accuracies of 93.4% and 91.7% are obtained for STARE dataset and ARIA dataset, respectively, that consists of many severely degraded images of pathological retinas that state-of-the-art methods cannot segment properly. Furthermore, the algorithm runs in 5 s, which is substantially faster than many of the state-of-the-art methods.
在计算机辅助诊断的框架下，本文提出了一种准确而高效的视盘（OD）检测和分割技术。设计了一种圆形变换，同时捕捉 OD 的圆形形状和 OD 边界的图像变化。对于每个视网膜图像像素，它评估了多个特定长度的均匀定向径向线段的图像变化。确定了所有径向线段上具有最大变化的像素，这些像素可进一步用于准确地定位 OD 中心和 OD 边界。实验表明，在 STARE 数据集、ARIA 数据集和 MESSIDOR 数据集上，OD 检测的准确率分别达到了 99.75%、97.5%和 98.77%，而 OD 中心误差在 STARE 数据集和 ARIA 数据集周围为六个像素左右，远小于现有方法的 14-29 个像素。此外，在 STARE 数据集和 ARIA 数据集上，OD 分割的准确率分别达到了 93.4%和 91.7%，这两个数据集包含了许多病理视网膜的严重退化图像，而现有方法无法正确分割这些图像。此外，该算法的运行速度为 5 秒，明显快于许多现有方法。
