摘要
针对人工手动提取视网膜血管工作量大,主观性强等问题,本文提出了一种将区域生长思想、脉冲耦合神经网络(PCNN)、高斯滤波器组及Gabor滤波器相结合的视网膜血管分割方法。首先将二维高斯滤波器组、二维Gabor匹配滤波器相结合,对视网膜血管区域进行形态匹配增强,提升血管与背景的对比度。然后将带有快速连接机制的PCNN与区域生长思想相结合,每次从未处理的像素点中选取亮度最大的作为种子,使用自适应的连接系数及停止条件,实现眼底图像中血管的自动分割。整个算法在DRIVE眼底数据库上的实验结果显示,平均准确度、灵敏度、特异性分别达到93.96%、78.64%、95.64%,分割结果中血管断点少,微小血管清晰,具有较好的应用前景。
Aiming at the problem of large workload and strong subjectivity for manual retinal vessels extraction, this paper proposes a retinal vessel segmentation method that combines regional growing strategy, pulse coupled neural network(PCNN), a Gaussian filter bank and a Gabor filter. First, 2D Gaussian filter bank and 2D Gabor filter are combined to enhance the shape retinal blood vessel region and strengthen the contrast between the blood vessel and the background. Then, PCNN with fast linking mechanism and region growing idea is implemented to achieve automatic retinal vessel segmentation in which the unprocessed pixel with highest intensity is set as the seed, and the adaptive linking weight and stop conditions are adopted. The experimental results on the DRIVE fundus database show that the average accuracy, sensitivity and specificity are 93.96%, 78.64%, 95.64%, respectively. The segmentation results have less vascular breakpoints and clear micro-vessels. This work has promising application value.
作者
徐光柱
王亚文
胡松
陈鹏
周军
雷帮军
Xu Guangzhu;Wang Yawen;Hu Song;Chen Peng;Zhou Jun;Lei Bangjun(College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, China;Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei 443002, China;Department of Diagnostic Ultrasound, the First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443002, China)
出处
《光电工程》
CAS
CSCD
北大核心
2019年第4期71-82,共12页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(61402259
61272236
U1401252)
宜昌市科技局项目(A19-302-13)~~
