IEEE J Biomed Health Inform. 2015 May;19(3):1118-28. doi: 10.1109/JBHI.2014.2335617.
This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.
本文提出了一种新颖的基于眼底照片的三段式血管分割算法。在第一阶段，对眼底图像的绿色通道进行预处理，经过高通滤波提取二值图像，并对形态重建后的增强图像进行另一次二值化处理以提取血管区域。接下来，提取两个二值图像中共有的区域作为主要血管。在第二阶段，使用基于像素邻域和一阶及二阶梯度图像提取的八组特征，通过高斯混合模型（GMM）分类器对两个二值图像中的所有剩余像素进行分类。在第三阶段的后处理中，将主要血管部分与分类后的血管像素相结合。与现有的监督分割方法相比，该算法对训练数据的依赖性较小，分割时间更短，在正常图像和有病变的图像上均能达到一致的血管分割精度。该算法在三个公共数据集 DRIVE、STARE 和 CHASE_DB1 上的平均分割时间分别为 3.1、6.7 和 11.7 秒，血管分割准确率分别为 95.2%、95.15%和 95.3%。
