基于2D Gabor小波与组合线检测算子的视网膜血管分割

单一的2D Gabor小波血管分割算法只考虑了图像滤波信息,忽略了血管形状和结构信息。为了更加精确快速地实现视网膜分割血管,提出了一种基于2D Gabor小波变换和组合线检测算子的视网膜血管分割方法。首先通过像素灰度值、4个尺度下的2D Gabor小波变换和组合线检测算子构造一个六维像素特征向量,然后使用贝叶斯高斯混合模型实现视网膜图像像素分类,最终实现血管分割。通过对通用的DRIVE眼底图像库中所有视网膜图像的实验仿真,结果表明算法获得了0.963 6的受试者特征工作曲线面积和0.948 6的准确率,优于单一的2D Gabor小波血管分割算法。

Retinal vessel segmentation based on 2D Gabor wavelet and combined line operators

Single 2D Gabor wavelet vessel segmentation algorithm only considers the image filtering information, ignoring the vascular shape and structure information. A novel segmentation method based on 2D Gabor wavelet and combined line operator was proposed for extracting the retinal vessels more accurately and rapidly from retinal fundus images. Firstly, we construct a pixel feature vector consisting of the pixel intensity, four features from Gabor wavelet transform in different scales and one feature from combined line operators. Then, the segmentation of retinal blood vessels is implemented by the proposed feature vector in conjunction with a Bayesian classifier with gaussian mixture model. The proposed method has been tested with all retinal images in the publicly available DRIVE database. Experiment results show that the proposed method achieves an area under the receiver operating characteristic curve of 0. 9636 and accuracy of 0. 9486, it is distinctly better than the single 2D Gabor wavelet vessel segmentation algorithm.