1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 "Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Central South University Changsha 410083, China;
3 College of Literature and Journalism, Central South University, Changsha 410083, China
Abstract Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification (AVC) method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc (OD) localization. Then, a region of interest (ROI) is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix (GLCM) features, and an adaptive local binary patten (A-LBP) feature by using a max-relevance and min-redundancy (mRMR) scheme. Finally, a feature-weighted K-nearest neighbor (FW-KNN) algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.
About author: Bei-Ji Zou received his B.S.degree in computer software from Zhejiang University,Hangzhou,in 1982,his M.S.and Ph.D.degrees in computer science and technology from Tsinghua University,Beijing,in 1984,and Hunan University,Changsha,in 2001,respectively.He joined the School of Computer and Communication at Hunan University,Changsha,in 1984,where he became an associate professor in 1997,and a professor in 2001.
Cite this article:
Bei-Ji Zou, Yao Chen, Cheng-Zhang Zhu, Zai-Liang Chen, Zi-Qian Zhang.Supervised Vessels Classification Based on Feature Selection[J] Journal of Computer Science and Technology, 2017,V32(6): 1222-1230
 Niemeijer M, van Ginneken B, Abràmoff M D. Automatic classification of retinal vessels into arteries and veins. In Proc. SPIE7260, Medical Imaging 2009:Computer-Aided Diagnosis, October 2009, p.72601F. Grisan E, Ruggeri A. A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In Proc. the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sept. 2003, pp.890-893. Hubbard L D, Brothers R J, King W N, Clegg L X, Klein R, Cooper L S et al. Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study. Ophthalmology, 1999, 106(12):2269-2280. Wong T Y, Knudtson M D, Klein R, Klein B E K, Meuer S M, Hubbard L D. Computer-assisted measurement of retinal vessel diameters in the Beaver Dam Eye Study:Methodology, correlation between eyes, and effect of refractive errors. Ophthalmology, 2004, 111(6):1183-1190. Aguilar W, Martinez-Perez M E, Frauel Y, Escolano F, Lozano M A, Espinosa-Romero A. Graph-based methods for retinal mosaicing and vascular characterization. In Proc. the 6th IAPR-TC-15 International Workshop on GraphBased Representations in Pattern Recognition, June 2007, pp.25-36. Rothaus K, Jiang X, Rhiem P. Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Image and Vision Computing, 2009, 27(7):864-875. Niemeijer M, Xu X, Dumitrescu A V, Gupta P, van Ginneken B et al. Automated measurement of the arteriolarto-venular width ratio in digital color fundus photographs. IEEE Transactions on Medical Imaging, 2011, 30(11):1941-1950. Dashtbozorg B, Mendonca A M, Campilho A. An automatic graph-based approach for artery/vein classification in retinal images. IEEE Transactions on Image Processing, 2014, 23(3):1073-1083. Estrada R, Allingham M J, Mettu P S, Cousins S W, Tomasi C, Farsiu S. Retinal artery-vein classification via topology estimation. IEEE Transactions on Medical Imaging, 2015, 34(12):2518-2534. Relan D, Ballerini L, Trucco E, MacGillivray T. Retinal vessel classification based on maximization of squared-loss mutual information. In Proc. Machine Intelligence and Signal Processing, October 2016, pp.77-84. Staal J, Abramoff M D, Niemeijer M, Viergever M A, Ginneken B V. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 2004, 23(4):501-509. Vijayakumar V, Koozekanani D D, White R, Kohler J, Roychowdhury S, Parhi K K. Artery/vein classification of retinal blood vessels using feature selection. In Proc. the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 2016, pp.1320-1323. Zhu C, Zou B, Zhao R et al. Retinal vessel segmentation in colour fundus images using extreme learning machine. Computerized Medical Imaging and Graphics, 2017, 55:68-77. Abdullah M, Fraz M M, Barman S A. Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm. https://peerj.com/articles/2003/, Sept. 2017. Telea A, van Wijk J J. An augmented fast marching method for computing skeletons and centerlines. In Proc. the Symposium on Data Visualisation, May 2002, pp.251-260. Foracchia M, Grisan E, Ruggeri A. Luminosity and contrast normalization in retinal images. Medical Image Analysis, 2005, 9(3):179-190. Soares J V B, Leandro J J G, Cesar R M, Jelinek H F, Cree M J. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, 2006, 25(9):1214-1222. Hanchuan P, Fuhui L, Ding C. Feature selection based on mutual information:Criteria of max-dependency, maxrelevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8):1226-1238. Chhabra S, Bhushan B. Supervised pixel classification into arteries and veins of retinal images. In Proc. Innovative Applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH), November 2014, pp.59-62.