[1]刘海坤,王健,杨嵩,等. 基于深度学习和二维高斯拟合的视网膜血管管径测量方法[J].中国医学物理学杂志,2019,36(2):171-179.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.010]
　LIU Haikun,WANG Jian,YANG Song,et al. Retinal vessel diameter measurement based on depth learning and two-dimensional Gaussian fitting[J].Chinese Journal of Medical Physics,2019,36(2):171-179.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.010]
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 基于深度学习和二维高斯拟合的视网膜血管管径测量方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第2期
页码:
171-179
栏目:
医学影像物理
出版日期:
2019-02-25

文章信息/Info

Title:
 Retinal vessel diameter measurement based on depth learning and two-dimensional Gaussian fitting
文章编号:
1005-202X（2019）02-0171-09
作者:
 刘海坤1; 王健2; 杨嵩1; 吴骏1; 尹昌顺1; 张凯1; 张震1; 裴新然1; 吴帅1
 1.天津工业大学电子与信息工程学院， 天津 300387； 2.中国人民解放军空军93756部队教研部电子教研室， 天津 300131
Author(s):
 LIU Haikun1;  WANG Jian2;  YANG Song1;  WU Jun1;  YIN Changshun1;  ZHANG Kai1;  ZHANG Zhen1;  PEI Xinran1;  WU Shuai1
 1. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China; 2. Department of Electronics, Chinese People’s Liberation Army Air Force 93756, Tianjin 300131, China
关键词:
 ; 视网膜血管; 全卷积神经网络; 管径测量; 二维高斯拟合
Keywords:
 ; Keywords: retinal vessel;  fully convolutional network;  diameter measurement;  two-dimensional Gaussian fitting
分类号:
R318；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2019.02.010
文献标志码:
A
摘要:
 糖尿病、高血压等疾病会引起视网膜血管的形状发生变化，眼底图像血管分割是疾病定量分析过程中的关键步骤，对临床疾病的分析和诊断具有指导意义。本文提出一种视网膜血管管径自动测量方法。首先，将通道特征图叠加，同时通过使用深度可分离卷积来增加网络深度，将二者引用于全卷积神经网络中对血管网络进行分割；然后在分割的血管网络基础上，利用形态学细化和最小二乘拟合求取血管的中心线和方向；最后根据血管横截面灰度值分布特性，利用二维高斯拟合对血管中心线和方向进行校正，得到准确的血管方向和中心线位置进而计算血管管径。利用本文方法分别对REVIEW数据库中的3个图像集进行测试，测量的管径均值的标准差接近专家测量的标准差，表明本文血管管径测量方法的准确率高，实验结果验证了本文方法的准确性。
Abstract:
 Abstract: Diseases such as diabetes and hypertension can lead to changes in the shape of retinal blood vessels. Segmentation of fundus images is critical in the quantitative analysis of diseases, which is instructive in the clinical analysis and diagnosis of diseases. Herein a method based on depth learning and two-dimensional Gaussian fitting is proposed to automatically measure the diameters of retinal vessels. Firstly, the connected channel signatures and the network depth which is increased by depthwise separable convolutions are applied into fully convolutional network to segment the vessel network. Then, morphological refinement and least-squares fitting are used to find the centerline and direction of blood vessels in the segmented vessel network. Finally, based on gray value distribution characteristics of blood vessel cross-section, two-dimension Gaussian fitting is used to correct the centerlines and directions of retinal vessels for obtaining the accurate centerlines and directions of retinal vessels and then measuring the diameters of retinal vessels. Three image sets in the REVIEW database is tested. The standard deviation obtained with the proposed method is close to that of the manual measurement, which indicates that the proposed method can achieve a high accuracy in vessel diameter measurement. The validity of the proposed method is proved by the test on the REVIEW database.

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备注/Memo

备注/Memo:
 【收稿日期】2018-08-25
【基金项目】国家级大学生创新创业训练计划项目（201710058038）；天津市应用基础与前沿技术研究计划一般项目（15JCYBJC16600）
【作者简介】刘海坤，研究方向：数字图像处理、人工神经网络，E-mail: airhaile@163.com；王健，研究方向：数字图像处理、模式识别，E-mail: 1360325496@qq.com；杨嵩，研究方向：数字图像处理、人工神经网络，E-mail: 15222003506@163.com
【通信作者】吴骏，研究方向：数字图像处理、模式识别与人工神经网络，E-mail: zhenkongwujun@163.com
更新日期/Last Update: 2019-02-26