一种视网膜血管自适应提取方法

为了快速有效地提取视网膜血管，根据视网膜图像的灰度分布特征，提出了一种新的基于自适应阈值化的血管提取方法。该方法是首先把图像划分成很多同样尺寸的小子图像，然后在每个子图像中分别计算局部阈值，并用该阈值分割该子图像。因为视网膜图像中血管和背景在局部范围内都比较均匀，所以在每个子图像中都存在一个局部阈值能够将其中的血管分割出来。采用的局部阈值计算方法不仅允许子图像可以取得很小，而且能够保证得到平方误差最小意义下的最优阈值。在阈值计算过程中，还用到一种基于过零点边缘检测技术的边缘追踪算法。最后还提出一种基于区域生长的特征综合方法，即通过综合两次阈值化分割得到的血管结构来清除碎片。多幅视网膜图像的实验证明，该方法的计算速度很快，并且可以提取包括细血管在内的绝大部分血管。

Extracting Blood Vessels in Retinal Images by Adaptive Thresholding

In terms of the special gray distribution in retinal images, a novel blood vessel extraction method based on adaptive thresholding is proposed in this paper. The whole image is divided into many small sub-images with identical dimension, and u threshold is calculated respectively in each sub-image for segmenting local blood vessels. Because both vessels and background are locally uniform in retinal images, there must be a threshold which is able to segment vessels precisely in a certain sub-image. The method employed for determining the local threshold not only allows sub-images to be very small, but also ensures the threshold to be optimal in the sense of least square error. A new edge tracking algorithm based on zero-crossing edge detection technique is applied in the process of threshold computing. Further more, a feature synthesis method based on region growing is presented, which is used to clear fragments in results of adaptive thresholding. The experiments on many retinal images indicate that this blood vessel extraction method is computational efficient and can extract most blood vessels including very small blood vessels.