视网膜血管的自动提取。
Automated extraction of retinal vasculature.
作者信息
Tan Jen Hong, Acharya U Rajendra, Chua Kuang Chua, Cheng Calvin, Laude Augustinus
机构信息
Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489.
Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489 and Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
出版信息
Med Phys. 2016 May;43(5):2311. doi: 10.1118/1.4945413.
PURPOSE
The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline.
METHODS
The algorithm starts by background correction. The corrected image is filtered with a bank of Gabor kernels, and the responses are consolidated to form a maximal image. After that, the maximal image is thinned to get a network of 1-pixel lines, analyzed and pruned to locate forks and form branches. Finally, the Ramer-Douglas-Peucker algorithm is used to determine salient points. When extraction is not satisfactory, the user simply shifts the salient points to edit the segmentation.
RESULTS
On average, the authors' extractions cover 93% of ground truths (on the Drive database).
CONCLUSIONS
By expressing retinal vasculature as a series of connected points, the proposed algorithm not only provides a means to edit segmentation but also gives knowledge of the shape of the blood vessels and their connections.
目的
作者提出一种算法，该算法能自动提取视网膜血管并提供一种简单的校正提取方法。该方法的输出是一个显著点网络，血管通过使用向心参数化的卡特穆尔-罗姆样条连接显著点来绘制。
方法
该算法首先进行背景校正。校正后的图像用一组伽柏核进行滤波，然后将响应合并以形成一个最大图像。之后，对最大图像进行细化以得到一个单像素线网络，进行分析和修剪以定位分叉并形成分支。最后，使用拉默-道格拉斯-佩克算法确定显著点。当提取效果不理想时，用户只需移动显著点来编辑分割。
结果
平均而言，作者的提取结果覆盖了93%的真值（在Drive数据库上）。
结论
通过将视网膜血管表示为一系列相连的点，所提出的算法不仅提供了一种编辑分割的方法，还给出了血管形状及其连接的信息。
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