基于Hesse矩阵和多尺度分析的视网膜动静脉血管管径测量方法

doi:10.11999/JEIT160165

Abstract
Figure/Table
References (15)
Related Citation (1)

Download: PDF (7575 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Many systemic diseases can cause changes of the diameters of retinal vessels and Arteriolar-to-Venular diameter Ratios (AVR), so it is of great importance to make quantitative analysis of the diameter of retinal vessels accurately in the diagnosis of the disease. An automatic method measuring the diameters of the artery and the vein and the AVR is proposed. Firstly, based on the segmentation of vascular network, the diameters of retinal vessels are measured according to the advantage of Hesse matrix for detecting line-like structure, and accurate localization of vascular direction with multi-scale analysis. Secondly, a General Regression Neural Network (GRNN) classifier is used to classify the artery and the vein points. Finally, the AVR in the Region Of Interest (ROI) is calculated. The validity of the proposed method is demonstrated by testing on the DRIVE and the REVIEW database.

Key words： Retinal vessels Hesse matrix Diameter measurement General Regression Neural Network (GRNN) Arteriolar-to-Venular diameter Ratios (AVR)

Received: 24 February 2016 Published: 08 September 2016

PACS:

TP391.4

Fund:

The National Natural Science Foundation of China (61401439), Tianjin Science and Technology Supporting Projection (14ZCZDGX00033), Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC 16600), Specialized Research Fund for the Doctoral Program of Higher Education of China (20131201110001)

Corresponding Authors: WU Jun E-mail: zhenkongwujun@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XIAO Zhitao
	CUI Ning
	WU Jun
	GENG Lei
	ZHANG Fang
	WEN Jia
	TONG Jun
	LIU Xiaoting
	YANG Song

Cite this article:

XIAO Zhitao,CUI Ning,WU Jun等. Measurement of Retinal Diameters of Artery and Vein Based on Hesse Matrix and Multi-scale Analysis[J]. JEIT, 2016, 38(11): 2871-2878.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160165 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2871

[1]	李春艳, 徐力. 糖尿病视网膜血管管径的研究进展[J]. 医学综述, 2015, 25(17): 3173-3176. doi: 10.3969/j.issn.1006-2084. 2015.17.038.
	LI Chunyan and XU Li. Research progress of diabetic retinal vascular caliber[J], Medical Recapitulate, 2015, 25(17): 3173-3176. doi: 10.3969/j.issn.1006-2084.2015.17.038.
[2]	NIEMEIJER M, XU X, DUMITRESCU A V, et al. Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs[J]. IEEE Transactions on Medical Imaging, 2011, 30(11): 1941-1950. doi: 10.1109/TMI.2011.2159619.
[3]	GRISAN E and RUGGERI A. A divide et impera strategy for automatic classification of retinal vessels into arteries and veins[C]. The 25th Annual International Conference of the IEEE in Medicine and Biology Society, Cancun, Mexico, 2003, 1: 890-893. doi: 10.1109/IEMBS.2003.1279908.
[4]	姚畅, 陈后金. 一种基于先验知识的视网膜血管宽度测量方法[J]. 中国科技论文在线, 2009, 4(1): 64-68. doi: 10.3969/ j.issn.2095-2783.2009.01.011.
	YAO Chang and CHEN Houjin. Measurement of retinal vessel widths based on prior knowledge[J]. Sciencepaper Online, 2009, 4(1): 64-68. doi: 10.3969/j.issn.2095-2783.2009. 01.011.
[5]	LOWELL J, HUNTER A, STEEL D, et al. Quantitative measurement of retinal vessel widths from fundus images based on 2D modelling[J]. IEEE Transactions on Medical Imaging, 2004, 23(10): 1196-1204. doi: 10.1109/TMI.2004. 830524.
[6]	LUPA S CU C A, TEGOLO D, and TRUCCO E. Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model[J]. Medical Image Analysis, 2013, 17(8): 1164-1180. doi: 10.1016/j.media. 2013.07.006.
[7]	FRANGI A F, NIESSEN W J, VINCKEN K L, et al. Multiscale Vessel Enhancement Filtering[M]. Berlin, Germany, Medical Image Computing and Computer-Assisted Interventation, Springer, 1998: 130-137.
[8]	张新红, 张帆, 崔延斌. 基于多尺度自适应滤波的DSA血管增强[J]. 计算机工程与应用, 2015, 51(14): 179-185. doi: 10. 3778/j.issn.1002-8331.1406-0457.
	ZHANG Xinhong, ZHANG Fan, and CUI Yanbin. Blood vessel enhancement algorithm for DSA images based on adaptive multi-scale filtering[J]. Computer Engineering and Applications, 2015, 51(14): 179-185. doi: 10.3778/j.issn. 1002- 8331.1406-0457.
[9]	CHEN Y, BARCELOS C A Z, and MAIR B A. Smoothing and edge detection by time-varying coupled nonlinear diffusion equations[J]. Computer Vision and Image Understanding, 2001, 82(2): 85-100. doi: 10.1006/cviu. 2001.0903.
[10]	严颂华, 吴世才, 吴雄斌. 基于神经网络的高频地波雷达目标到达角估计[J]. 电子与信息学报, 2008, 30(2): 339-342.
	YAN Songhua, WU Shicai, and WU Xiongbin. DOA estimation based on neural network for HFGWR[J]. Journal of Electronics & Information Technology, 2008, 30(2): 339-342.
[11]	STAAL J, ABRAMOFF M D, NIEMEIJER M, et al. DRIVE [OL]. http://www.isi.uu.nl/Research/Databases/DRIVE/, 2004.
[12]	LI X, XIAO Z, and ZHANG F, et al. Fundus optic disc localization method based on phase congruency[J]. International Journal of Digital Content Technology and Its Applications, 2013, 7(4): 726-733.
[13]	ZHANG B, ZHANG L, ZHANG L, et al. Retinal vessel extraction by matched filter with first-order derivative of Gaussian[J]. Computers in Biology and Medicine, 2010, 40(4): 438-445. doi: 10.1016/j.compbiomed.2010.02.008.
[14]	ZHAO Y Q, WANG X H, WANG X F, et al. Retinal vessels segmentation based on level set and region growing[J]. Pattern Recognition, 2014, 47(7): 2437-2446. doi: 10.1016/j. patcog.2014.01.006.
[15]	AL-DIRI B, HUNTER A, and STEEL D. Review[OL]. http://reviewdb.lincoln.ac.uk/, 2008.