曹新容,薛岚燕,林嘉雯,余轮.基于匹配滤波和自动阈值的眼底血管分割方法[J].电子测量与仪器学报,2017,31(1):51-57
基于匹配滤波和自动阈值的眼底血管分割方法
Retinal vessel segmentation method based on matched filtering and automatic threshold
  
DOI:10.13382/j.jemi.2017.01.008
中文关键词:  匹配滤波  血管分割  对比度  阈值
英文关键词:matched filtering  retinal vessel segmentation  contrast  threshold
基金项目:福建省中青年教师教育科研项目(JAT160398)资助
作者单位
曹新容 1. 福州大学物理与信息工程学院福州350116; 2. 闽江学院福州350121;3. 福建省信息处理与智能控制重点实验室福州350121 
薛岚燕 福州大学物理与信息工程学院福州350116 
林嘉雯 福州大学物理与信息工程学院福州350116 
余轮 福州大学物理与信息工程学院福州350116 
AuthorInstitution
Cao Xinrong 1. College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China;2. Minjiang University, Fuzhou 350121, China; 3. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Fuzhou 350121, China 
Xue Lanyan College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China 
Lin Jiawen College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China 
Yu Lun College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China 
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中文摘要:
      提出一种快速、简便、高效的眼底血管分割方法。分析眼底图像的灰度值分布和对比度变化,利用匹配滤波克服背景干扰,消除噪声影响,达到灰度均衡,实现眼底图像的亮度归一化。估计眼底图像中背景像素所占比例,利用直方图自动选择阈值,完成对眼底图像中血管的有效分割。在公开的眼底图像数据库上进行测试,该方法对眼底血管分割具有较好的性能指标。实验表明,提出的基于匹配滤波和阈值优化的眼底血管分割方法,准确率高、复杂度低,对眼科疾病的计算机辅助诊断有一定的实用价值。
英文摘要:
      A simple, rapid and efficient retinal vessels segmentation method is proposed. After a general analysis on gray value distribution and contrast changes of fundus images, the standardizing fundus images are obtained by using the matched filtering technique to overcome the interference of background and noise. Then, a threshold can be automatically selected to achieve the effective segmentation of blood vessels in the fundus images by estimating the proportion of the background pixels. A lot of tests show that the good performance is achieved in the public fundus images database. The experiment shows that the proposed method based on matched filtering and automatic threshold has strong practicability and high accuracy. It is useful for computer aided diagnosis of ocular diseases.
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