Retinal vessel segmentation using modified symmetrical local threshold

Retinal vessel segmentation is important for the identification of many diseases including glaucoma, hypertensive retinopathy, diabetes, and hypertension. Moreover, retinal vessel diameter is associated with cardiovascularmortality. Accurate detection of blood vessels improves the detection of exudates in color fundus images, as well asdetection of the retinal nerve, optic disc, or fovea. A retinal vessel is a darker stripe on a lighter background. Thus, theobjective is very similar to the lane detection task for intelligent vehicles. A lane on a road is a light stripe on a darkerbackground (i.e. asphalt). For lane detection, the symmetrical local threshold (SLT) is found to be the most robustfeature extractor among the tested algorithms in the road marking (ROMA) dataset. Unfortunately, the SLT cannot beapplied directly for retinal vessel segmentation. The SLT is a 1D filter and is designed for detecting vertical or close tovertical light stripes with predictable width. In this paper, the SLT is modified to detect dark stripes and four kernels,instead of one, are designed to detect both vertical and horizontal features of a retinal vessel with variable thickness. Theproposed algorithm is tested using the High Resolution Fundus (HRF) image database and the accuracy is estimated tobe 95.53%. Furthermore, when tested with the Digital Retinal Images for Vessel Segmentation (DRIVE) database, theaccuracy is estimated to be 93.69%.

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[1] Grisan E, Foracchia M, Ruggeri A. A novel method for the automatic grading of retinal vessel tortuosity. IEEE Transactions on Medical Imaging 2008; 27 (3): 310-319.
[2] Cheung CY, Zheng Y, Hsu W, Lee ML, Lau QP et al. Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology 2011; 118 (5): 812-818.
[3] Youssef D, Solouma NH. Accurate detection of blood vessels improves the detection of exudates in color fundus images. Computer Methods and Programs in Biomedicine 2012; 108 (3): 1052-1061.
[4] Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J et al. Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Processing 2013; 7 (4): 373-383.
[5] Niemeijer M, Abramoff MD, Van Ginneken B. Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Transactions on Medical Imaging 2007; 26 (1): 116-127.
[6] Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka et al. Blood vessel segmentation methodologies in retinal images- a survey. Computer Methods and Programs in Biomedicine 2012; 108 (1): 407-433.
[7] Gang L, Chutatape O, Krishnan SM. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order gaussian filter. IEEE Transactions on Biomedical Engineering 2002; 49 (2): 168-172.
[8] Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 2000; 19 (3): 203-210.
[9] Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 2004; 23 (4): 501-509.
[10] Al-Rawi M, Karajeh H. Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Computer Methods and Programs in Biomedicine 2007; 87 (3): 248-253.
[11] Vlachos M, Dermatas E. Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics 2010; 34 (3): 213-227.
[12] Oliveira AFM, Pereira SRM, Silva CAB. Retinal vessel segmentation based on fully convolutional neural networks. Expert Systems with Applications 2018; 112: 229-242.
[13] Dasgupta A, Singh S. A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: 14th IEEE International Symposium on Biomedical Imaging (ISBI); Melbourne, VIC, Australia; 2017. pp. 248-251.
[14] Zhu C, Zou B, Zhao R, Cui J, Duan X et al. Retinal vessel segmentation in colour fundus images using extreme learning machine. Computerized Medical Imaging and Graphics 2017; 55: 68-77.
[15] Yu H, Barriga S, Agurto C, Zamora G, Bauman W et al. Fast vessel segmentation in retinal images using multiscale enhancement and second-order local entropy. Proceedings SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis 2012; 8315: 83151B.
[16] Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E. Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE Journal of Biomedical and Health Informatics 2016; 20 (4): 1129-1138.
[17] Yang Y, Shao F, Fu Z, Fu R. Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features. Signal, Image and Video Processing 2019; 13: 1529-1537.
[18] Al-Diri B, Hunter A, Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Transactions on Medical Imaging 2009; 28 (9): 1488-1497.
[19] Palomera-Perez MA, Martinez-Perez ME, Benitez-Perez H, Ortega-Arjona JL. Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Transactions on Information Technology in Biomedicine 2009; 14 (2): 500-506.
[20] Zhang B, Zhang L, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Computers in Biology and Medicine 2010; 40 (4): 438-445.
[21] Marín D, Aquino A, Gegundez-Arias ME, Bravo JM. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Transactions on Medical Imaging 2010; 30 (1): 146-158.
[22] Schreiber D, Alefs B, Clabian M. Single camera lane detection and tracking. In: IEEE Conference on Intelligent Transportation Systems; Vienna, Austria; 2005. pp. 302-307.
[23] Wang Y, Lee SC. A fast method for automated detection of blood vessels in retinal images. In: IEEE Conference on Signals, Systems & Computers; Pacific Grove, CA, USA; 1997. pp. 1700-1704.
24] Broggi A, Cappalunga A, Caraffi C, Cattani S, Ghidoni S et al. Terramax vision at the urban challenge 2007. IEEE Transactions on Intelligent Transportation Systems 2010; 11 (1): 194-205.
[25] McCall JC, Trivedi MM. An integrated, robust approach to lane marking detection and lane tracking. In: IEEE Conference on Intelligent Vehicles; Parma, Italy; 2004. pp. 533-537.
[26] McCall JC, Trivedi MM. Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Transactions on Intelligent Transportation Systems 2006; 7 (1): 20-37.
[27] López A, Serrat J, Canero C, Lumbreras F, Graf T. Robust lane markings detection and road geometry computation. International Journal of Automotive Technology 2010; 11 (3): 395-407.
[28] Veit T, Tarel JP, Nicolle P, Charbonnier P. Evaluation of road marking feature extraction. In: IEEE Conference on Intelligent Transportation Systems; Beijing, China; 2008. pp. 174-181
[29] Ozgunalp U, Fan R, Ai X, Dahnoun N. Multiple lane detection algorithm based on novel dense vanishing point estimation. IEEE Transactions on Intelligent Transportation Systems 2017; 18 (3): 621-632.
[30] Dong S, Peng C. A lane detection method based on track management approach. In: IEEE Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI); Beijing, China; 2014. pp. 1-6.
[31] Freeman WT, Adelson EH. The design and use of steerable filters. IEEE Transactions on Pattern Analysis & Machine Intelligence 1991; 9: 891-906.
[32] Ozgunalp U, Dahnoun N. Robust lane detection & tracking based on novel feature extraction and lane categorization. In: IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP); Florence, Italy; 2014. pp. 8129-8133.