基于SVM的眼底血管分割技术

Eye fundus vessel segmentation technology based on SVM

针对眼底中小血管提取的问题,提出了一种基于支持向量机的眼底血管分割方法。首先,采用高斯匹配滤波器对眼底图像进行滤波,增强图像对比度;然后,为了加快滤波器的运算速度,提出一种改进的滤波方法,只需像素与最佳匹配模板做卷积;最后,为了提高算法的分类性能,采用均值漂移算法先对滤波后的图像预分类。仿真结果表明提出方法能更准确地分割出眼底血管网络,特别是对中小血管的分割更加精确。

An improved retinal vessel segmentation algorithm based on SVM was proposed to solve the problem that it is difficult to extract medium ＆ small blood vessels at eye fundus. Firstly,the Gauss matched filter was used to filter retinal images and enhance the contrast ratio of images ; then,in order to improve the efficiency of the filter, an improved method of Gaussian matched filter was put forward,which only needs the convolution of pixels and the optimum matching template; finally,in order to improve the classification performance of the algorithm,the mean drifting algorithm was used to preclassify the filtered image. The simulation results show that,by adopting the pro-posed method,the retinal vascular network can be segmented more accurately, especially,is is more accurate for the segment of medium ＆ small blood vessels.