Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India.
Comput Biol Med. 2016 Mar 1;70:174-189. doi: 10.1016/j.compbiomed.2015.12.018. Epub 2016 Jan 13.
This paper proposes an automatic blood vessel extraction method on retinal images using matched filtering in an integrated system design platform that involves curvelet transform and kernel based fuzzy c-means. Since curvelet transform represents the lines, the edges and the curvatures very well and in compact form (by less number of coefficients) compared to other multi-resolution techniques, this paper uses curvelet transform for enhancement of the retinal vasculature. Matched filtering is then used to intensify the blood vessels' response which is further employed by kernel based fuzzy c-means algorithm that extracts the vessel silhouette from the background through non-linear mapping. For pathological images, in addition to matched filtering, Laplacian of Gaussian filter is also employed to distinguish the step and the ramp like signal from that of vessel structure. To test the efficacy of the proposed method, the algorithm has also been applied to images in presence of additive white Gaussian noise where the curvelet transform has been used for image denoising. Performance is evaluated on publicly available DRIVE, STARE and DIARETDB1 databases and is compared with the large number of existing blood vessel extraction methodologies. Simulation results demonstrate that the proposed method is very much efficient in detecting the long and the thick as well as the short and the thin vessels with an average accuracy of 96.16% for the DRIVE and 97.35% for the STARE database wherein the existing methods fail to extract the tiny and the thin vessels.
本文提出了一种基于视网膜图像的自动血管提取方法，该方法在一个集成的系统设计平台中使用匹配滤波，该平台涉及曲线波变换和基于核的模糊 c 均值。由于曲线波变换能够很好地表示线、边缘和曲率，并且与其他多分辨率技术相比，它的表示形式紧凑（系数数量较少），因此本文使用曲线波变换来增强视网膜血管。然后使用匹配滤波来增强血管的响应，再通过基于核的模糊 c 均值算法将血管轮廓从背景中提取出来，该算法通过非线性映射实现。对于病理图像，除了匹配滤波外，还使用拉普拉斯高斯滤波器来区分阶跃和斜坡信号与血管结构的信号。为了测试所提出方法的效果，该算法还应用于存在加性高斯白噪声的图像中，其中使用曲线波变换进行图像去噪。在公共可用的 DRIVE、STARE 和 DIARETDB1 数据库上进行性能评估，并与大量现有的血管提取方法进行比较。仿真结果表明，该方法在检测长而粗的血管以及短而细的血管方面非常有效，在 DRIVE 数据库中的平均准确率为 96.16%，在 STARE 数据库中的平均准确率为 97.35%，而现有方法无法提取细小的血管。
