Ridge-based vessel segmentation in color images of the retina
Ridge-based vessel segmentation in color images of the retina
复制标题
DOI：
10.1109/tmi.2004.825627
复制
发表时间：
2004-04-01
影响因子：
10.6
通讯作者：
van Ginneken, B
中科院分区：
文献类型：
--
作者：
Staal, J;Abràmoff, MD;van Ginneken, B
提出了一种在视网膜二维彩色图像中自动分割血管的方法。该方法可用于视网膜图像的计算机分析，例如在糖尿病视网膜病变的自动筛查中。该系统基于图像脊的提取，这些脊大致与血管中心线重合。这些脊被用于构成线元形式的基元。通过将每个图像像素分配给最近的线元，用线元将图像划分为小块。每个线元为其对应的小块构成一个局部坐标系。对于每个像素，计算利用小块和线元特性的特征向量。使用k近邻分类器和顺序前向特征选择对特征向量进行分类。该算法在一个由40幅手动标记图像组成的数据库上进行了测试。该方法在受试者工作特征曲线下的面积达到0.952。将该方法与胡佛等人[1]和江等人[2]最近发表的两种基于规则的方法进行了比较。结果表明，我们的方法明显优于这两种基于规则的方法（p < 0.01）。我们的方法准确率为0.944，而第二位观察者的准确率为0.947。
A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. [1] and Jiang et al. [2]. The results show that our method is significantly better than the two rule-based methods (p < 0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.