病变视网膜图像血管网络的自动分割

现有的视网膜血管分割方法大多只针对正常的视网膜图像进行分割，不能实现对发生病变的视网膜图像的分割.为此，提出了一种新的病变视网膜图像血管网络分割方法.该方法首先采用向量场散度方法获得病变视网膜图像中大部分血管的中心线，然后计算出中心线上各像素点的方向信息并采用改进的定向局部对比度方法检测出中心线两侧的血管像素，最后对获得的血管段末端进行反向外推追踪，分割出最终的血管网络.通过对通用的STARE眼底图像库中所有病变视网膜图像的实验仿真，结果表明本文算法获得了0.9426的ROC曲线面积和0.9502的准确率，算法性能明显优于Hoover算法和Benson等提出的算法.此外，本文算法还克服了Benson算法的局限性，对不同类型的病变视网膜图像都具有较好的鲁棒性.

Automated Blood Vessel Network Segmentation in Pathological Retinal Images

Most existing retinal blood vessels segmentation methods are robust only for normal retinal images,but not for pathological retinal images.In this paper,a new method for segmenting blood vessels in pathological retinal images is proposed.Firstly,the divergence of the vector field is used to locate most centerlines of pathological retinal image.Then the directional information of each pixel in centerlines is computed and the pixels around the centerlines are detected by modified directional local contrast method.Finally,the whole blood vessel network is obtained via reverse tracing at the end of each blood vessel segment.The proposed method has been tested with all the pathological retinal images in the publicly available STARE database.Experiment results show that the proposed method achieves an area under the receiver operating characteristic curve of 09426 and accuracy of 09502,it is distinctly better than methods of Hoover and Benson et al.Moreover,the proposed method has overcome the limitation of method of Benson et al,and is robust for all kinds of pathological retinal images.