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基于Hessian的方向自适应Gabor小波的视网膜血管分割

Retinal Blood Vessel Segmentation Using Hessian Based Orientational Adaptive Gabor Wavelet

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摘要

视网膜血管分割是构建眼底图像分析和计算机辅助疾病诊断系统的关键环节。提出了一种基于Hessian的方向自适应Gabor小波的视网膜血管分割方法,根据Hessian矩阵的本征向量获得血管走向,并将其作为Gabor小波变换的方向角;提取出4个尺度的方向自适应Gabor小波特征,结合Hessian矩阵的大本征值构建5维的视网膜血管特征;采用支持向量机进行眼底图像像素分类实现血管分割。所提方法能准确感知血管方向,只需计算此方向下Gabor小波的滤波响应,减小了特征提取的计算量,实现了Hessian矩阵大本征值与Gabor小波特征较好的互补性。利用所提方法在DRIVE数据库进行实验,获得较好的分割性能,所提方法对细小血管的提取和对分叉、交叉处血管点的检测表现出良好的效果。

Abstract

Retinal blood vessel segmentation is one of the key components in the construction of fundus image analysis and computer-aided disease diagnosis systems. In this paper, a method of retinal blood vessel segmentation using Hessian-based orientational adaptive Gabor wavelet is proposed. According to the eigenvector of the Hessian matrix, the orientation of the blood vessel is obtained and set as the direction angle of the Gabor wavelet transform. By extracting the features of the four-scale orientational adaptive Gabor wavelet in combination with the large eigenvector of the Hessian matrix, five-dimensional retinal blood vessel features are constructed. The segmentation of the blood vessels can thus be realized through classifying the pixels of the fundus images with the support vector machine. The proposed method can accurately sense the direction of the blood vessel by only calculating the filtering response of the Gabor wavelet in this direction, which reduces the computational complexity of the feature extraction and achieves good complimentary between the large eigenvalues of the Hessian matrix and Gabor wavelet features. Experiments using the proposed method are performed on the DRIVE database to obtain better segmentation performance. The proposed method shows good performance in the extraction of small blood vessels and in the detection of vascular points at bifurcation and intersection.

中国激光微信矩阵
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中图分类号:TN911.73

DOI:10.3788/LOP57.081023

所属栏目:图像处理

基金项目:国家自然科学基金;

收稿日期:2019-07-22

修改稿日期:2019-09-24

网络出版日期:2020-04-01

作者单位    点击查看

王文斌
李灿标
郑楚君

联系人作者:郑楚君(cjzheng@scnu.edu.cn)

备注:国家自然科学基金;

【1】Chaudhuri S, Chatterjee S, Katz N, et al. Detection of blood vessels in retinal images using two-dimensional matched filters [J]. IEEE Transactions on Medical Imaging. 1989, 8(3): 263-269.

【2】Hoover A D, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response [J]. IEEE Transactions on Medical imaging. 2000, 19(3): 203-210.

【3】Sofka M, Stewart C V. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures [J]. IEEE Transactions on Medical Imaging. 2006, 25(12): 1531-1546.

【4】Li Q, You J, Zhang D. Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses [J]. Expert Systems with Applications. 2012, 39(9): 7600-7610.

【5】Wang X H, Zhao Y Q, Liao M, et al. Automatic segmentation for retinal vessel based on multi-scale 2D Gabor wavelet [J]. Acta Automatica Sinica. 2015, 41(5): 970-980.
王晓红, 赵于前, 廖苗, 等. 基于多尺度2D Gabor小波的视网膜血管自动分割 [J]. 自动化学报. 2015, 41(5): 970-980.

【6】Meng L, Liu J, Cao H, et al. Retinal vessel segmentation based on Frangi filter and Otsu algorithm [J]. Laser & Optoelectronics Progress. 2019, 56(18): 181004.
孟琳, 刘静, 曹慧, 等. 基于Frangi滤波器和Otsu视网膜血管分割 [J]. 激光与光电子学进展. 2019, 56(18): 181004.

【7】Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification [J]. IEEE Transactions on Medical Imaging. 2007, 26(10): 1357-1365.

【8】Soares J V B, Leandro J J G, Cesar R M, et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification [J]. IEEE Transactions on Medical Imaging. 2006, 25(9): 1214-1222.

【9】Tang S Y, Lin T, Yang J, et al. Retinal vessel segmentation using supervised classification based on multi-scale vessel filtering and Gabor wavelet [J]. Journal of Medical Imaging and Health Informatics. 2015, 5(7): 1571-1574.

【10】Cai Y H, Gao X R, Qiu C Y, et al. Retinal vessel segmentation method with efficient hybrid features fusion [J]. Journal of Electronics & Information Technology. 2017, 39(8): 1956-1963.
蔡轶珩, 高旭蓉, 邱长炎, 等. 一种混合特征高效融合的视网膜血管分割方法 [J]. 电子与信息学报. 2017, 39(8): 1956-1963.

【11】Zhu C Z, Cui J K, Zou B J, et al. Retinal vessel segmentation based on multiple feature fusion and random forest [J]. Journal of Computer-Aided Design & Computer Graphics. 2017, 29(4): 584-592.
朱承璋, 崔锦恺, 邹北骥, 等. 基于多特征融合和随机森林的视网膜血管分割 [J]. 计算机辅助设计与图形学学报. 2017, 29(4): 584-592.

【12】Liang L M, Liu B W, Yang H L, et al. Supervised blood vessel extraction in retinal images based on multiple feature fusion [J]. Chinese Journal of Computers. 2018, 41(11): 2566-2580.
梁礼明, 刘博文, 杨海龙, 等. 基于多特征融合的有监督视网膜血管提取 [J]. 计算机学报. 2018, 41(11): 2566-2580.

【13】Wu C Y, Yi B S, Zhang Y G, et al. Retinal vessel image segmentation based on improved convolutional neural network [J]. Acta Optica Sinica. 2018, 38(11): 1111004.
吴晨玥, 易本顺, 章云港, 等. 基于改进卷积神经网络的视网膜血管图像分割 [J]. 光学学报. 2018, 38(11): 1111004.

【14】Zheng T Y, Tang C, Lei Z K. Multi-scale retinal vessel segmentation based on fully convolutional neural network [J]. Acta Optica Sinica. 2019, 39(2): 0211002.
郑婷月, 唐晨, 雷振坤. 基于全卷积神经网络的多尺度视网膜血管分割 [J]. 光学学报. 2019, 39(2): 0211002.

【15】Yin B J, Li H T, Sheng B, et al. Vessel extraction from non-fluorescein fundus images using orientation-aware detector [J]. Medical Image Analysis. 2015, 26(1): 232-242.

【16】Niemeijer M. Staal J, van Ginneken B, et al. Comparative study of retinal vessel segmentation methods on a new publicly available database [J]. Proceedings of SPIE. 2004, 5370: 648-656.

【17】Arneodo A, Decoster N, Roux S G. A wavelet-based method for multifractal image analysis. I. Methodology and test applications on isotropic and anisotropic random rough surfaces [J]. The European Physical Journal B-Condensed Matter and Complex Systems. 2000, 15(3): 567-600.

【18】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.

引用该论文

Wang Wenbin,Li Canbiao,Zheng Chujun. Retinal Blood Vessel Segmentation Using Hessian Based Orientational Adaptive Gabor Wavelet[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081023

王文斌,李灿标,郑楚君. 基于Hessian的方向自适应Gabor小波的视网膜血管分割[J]. 激光与光电子学进展, 2020, 57(8): 081023

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