Brain-inspired computer vision with applications to pattern recognition and computer-aided
diagnosis of glaucoma
Guo, Jiapan
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
2017
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Guo, J. (2017). Brain-inspired computer vision with applications to pattern recognition and computer-aided
diagnosis of glaucoma. University of Groningen.
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
Abdel-Razik Youssif, A.-H., Ghalwash, A. and Abdel-Rahman Ghoneim, A.: 2008, Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter, IEEE Transactions on Medical Imaging 27(1), 11–18.
Abr`amoff, M. D., Alward, W. L. M., Greenlee, E. C., Shuba, L., Kim, C. Y., Fingert, J. H. and Kwon, Y. H.: 2007, Automated segmentation of the optic disc from stereo color pho-tographs using physiologically plausible features, Investigative Ophthalmology and Visual Science 48(4), 1665–1673.
Akram, M. U., Tariq, A., Khalid, S., Javed, M. Y., Abbas, S. and Yasin, U. U.: 2015, Glau-coma detection using novel optic disc localization, hybrid feature set and classification techniques, Australasian Physical & Engineering Sciences in Medicine 38(4), 643–655.
Attneave, F.: 1954, Some informational aspects of visual perception, Psychological Review
61(3), 183–193.
Azzopardi, G. and Petkov, N.: 2011, Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters, Computer Analysis of Images and Patterns, Vol. 6854 of Lecture Notes in Com-puter Science, Springer Berlin Heidelberg, pp. 451–459.
Azzopardi, G. and Petkov, N.: 2012, A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model, Biological Cybernetics 106(3), 177–189. Azzopardi, G. and Petkov, N.: 2013a, A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits, Computer Analysis of Images and Patterns (CAIP, York, United Kingdom), Vol. 8048 of Lecture Notes in Computer Science, Springer International Publishing,, pp. 9–16.
Azzopardi, G. and Petkov, N.: 2013b, Trainable COSFIRE filters for keypoint detection and pattern recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 35(2), 490– 503.
Azzopardi, G. and Petkov, N.: 2014, Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models, Frontiers in Computational Neuroscience 8(80).
Azzopardi, G., Rodr´ıguez-S´anchez, A., Piater, J. and Petkov, N.: 2014, A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection, Plos One 9(7), e98424.
Azzopardi, G., Strisciuglio, N., Vento, M. and Petkov, N.: 2015, Trainable COSFIRE filters for vessel delineation with application to retinal images, Medical Image Analysis 19(1), 46–57. Beaudet, P. R.: 1978, Rotationally invariant image operators, Proceedings of the 4th International
Joint Conference on Pattern Recognition, pp. 579–583.
Bhuiyan, A., Nath, B., Chua, J. J. and Ramamohanarao, K.: 2007, Automatic Detection of Vas-cular Bifurcations and Crossovers from Color Retinal Fundus Images, SITIS, IEEE Com-puter Society, pp. 711–718.
Bock, R., Meier, J., Ny ´ul, L., Hornegger, J. and Michelson, G.: 2010, Glaucoma risk index: au-tomated glaucoma detection from color fundus images, Medical Image Analysis 14(3), 471– 481.
Bolz, J. and Gilbert, C.: 1986, Generation of end-inhibition in the visual cortex via interlaminar connections, Nature 320(6060), 362–365.
Brincat, S. and Connor, C.: 2004, Underlying principles of visual shape selectivity in posterior inferotemporal cortex, Nature Neuroscience 7(8), 880–886.
Carmona, E. J., Rinc ´on, M., Garc´ıa-Feijo ´o, J. and Mart´ınez-de-la Casa, J. M.: 2008, Identi-fication of the optic nerve head with genetic algorithms, Artificial Intelligence in Medicine
43(3), 243–259.
Chapman, N., Dell’omo, G., Sartini, M., Witt, N., Hughes, A., Thom, S. and Pedrinelli, R.: 2002, Peripheral vascular disease is associated with abnormal arteriolar diameter relation-ships at bifurcations in the human retina, Clinical Science 103(2), 111–116.
Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y. and Liu, J.: 2015, Glaucoma detection based on deep convolutional neural network, Proc 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 715–718.
Chr´astek, R., Wolf, M., Donath, K., Michelson, G. and Niemann, H.: 2002, Optic disc segmen-tation in retinal images, Bildverarbeitung f ˜AŒr die Medizin 2002, Springer Berlin Heidelberg, pp. 263–266.
Dalal, N. and Triggs, B.: 2005, Histograms of oriented gradients for human detection, Com-puter Vision and Pattern Recognition, 2005. CVPR 2005. IEEE ComCom-puter Society Conference on, Vol. 1, pp. 886–893.
Daugman, J. G.: 1985, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, Journal of the Optical Society of America A: Optics, Image Science, and Vision 2(7), 1160–1169.
Davies, E.: 2004, Machine Vision: Theory, Algorithms, Practicalities, Morgan Kaufmann Pub-lishers Inc. 3rd Edition, 283–313.
de Vries, M. M., Stoutenbeek, R., M ¨uskens, R. P. H. M. and Jansonius, N. M.: 2012, Glaucoma Screening During Regular Optician Visits: The Feasibility and Specificity of Screening in Real Life, Acta Ophthalmologica 90(2), 115–121.
Decenci´ere, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., Charton, B. and Klein, J.-C.: 2014, Feedback on a publicly dis-tributed database: the Messidor database, Image Analysis and Stereology 33(3), 231–234. D’Errico, J.: 2004, Inpaint nans, MATLAB Central File Exchange .
Fern´andez-Robles, L., Azzopardi, G., Alegre, E. and Petkov, N.: 2015, Cutting edge localisa-tion in an edge profile milling head, Computer Analysis of Images and Patterns, Vol. 9257 of Lecture Notes in Computer Science, Springer International Publishing, pp. 336–347.
Fidler, S., Berginc, G. and Leonardis, A.: 2006, Hierarchical Statistical Learning of Generic Parts of Object Structure, IEEE Computer Society, pp. 182–189.
Fixot, R.: 1957, , American Journal of Ophthalmology .
Florack, L., Ter Haar Romeny, B. M., Koenderink, J. J. and Viergever, M. A.: 1994, General intensity transformations and differential invariants, Journal of Mathematical Imaging and Vision 4(2), 171–187.
Foracchia, M., Grisan, E. and Ruggeri, A.: 2004, Detection of optic disc in retinal images by means of a geometrical model of vessel structure, IEEE Trans. Med. Imaging 23(10), 1189– 1195.
Foster, P. J., Buhrmann, R., Quigley, H. A. and Johnson, G. J.: 2002, The definition and classi-fication of glaucoma in prevalence surveys, British Journal of Ophthalmology 86(2), 238–242. Gabor, D.: 1946, Theory of Communication, Journal of the Institution of Electrical Engineers
93, 429–441.
Grady, D.: 1993, The Vision Thing: Mainly in the Brain, Discovery .
Grigorescu, C., P. N. W. M.: 2003a, Contour detection based on nonclassical receptive field inhibition, IEEE Transactions on Image Processing 12(7), 729–739.
Grigorescu, C., P. N. W. M.: 2003b, The Role of Non-CRF inhibition in contour detection, Journal of Computer Graphics, Visualization, and Computer Vision 11(2), 197–204.
Grigorescu, S., P. N. K. P.: 2002, Comparison of texture features based on Gabor filters. IEEE Transactions on Image Processing, IEEE Transactions on Image Processing 11(10), 1160–1167.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Rajiv, R., Nelson, P. C., Mega, J. L. and Webster, D. R.: 2016, Development and validation of a deep learning algorithm for detec-tion of diabetic retinopathy in retinal fundus photographs, JAMA 316(22), 2402–2410. Guo, J., Shi, C., Azzopardi, G. and Petkov, N.: 2015a, Recognition of Architectural and
Elec-trical Symbols by COSFIRE Filters with Inhibition, Computer Analysis of Images and Patterns 2015, Vol. 9257 of Lecture Notes in Computer Science, pp. 348–358.
Guo, J., Shi, C., Azzopardi, G. and Petkov, N.: 2015b, Recognition of architectural and elec-trical symbols by COSFIRE filters with inhibition, Computer Analysis of Images and Patterns, Vol. 9257 of Lecture Notes in Computer Science, Springer International Publishing, pp. 348– 358.
Guo, J., Shi, C., Azzopardi, G. and Petkov, N.: 2016, Inhibition-augmented trainable COS-FIRE filters for keypoint detection and object recognition, Machine Vision and Applications
27(8), 1197–1211.
Guo, J., Shi, C., Jansonius, N. M. and Petkov, N.: 2015, Automatic Optic Disk Localization and Diameter Estimation in Retinal Fundus Images, 8th GI Conference on Autonomous Systems, Vol. 842, pp. 70–79.
H. Bay, A. Ess, T. T. and Gool, L. V.: 2008, Speeded-Up Robust Features (SURF), Comput. Vis. Image Underst. 110(3), 346–359.
Harris, C. and Stephens, M.: 1988, A Combined Corner and Edge Detection, Proceedings of The Fourth Alvey Vision Conference, pp. 147–151.
Hoover, A. and Goldbaum, M.: 2003, Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels, IEEE Transactions on Medical Imaging 22(8), 951– 958.
Hubel, D.: 1988, Eye, brain, and vision, Scientific American, New York 22.
Hubel, D. H. and Wiesel, T. N.: 1962, Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex, Journal of Physiology (London) 160, 106–154.
Hubel, D. H. and Wiesel, T. N.: 1968, Receptive Fields and Functional Architecture of Monkey Striate Cortex, Journal of Physiology (London) 195, 215–243.
Hubel, D. H. and Wiesel, T. N.: 1974, Sequence regularity and geometry of orientation columns in the monkey striate cortex, The Journal of Comparative Neurology 158(3), 267–293. Jonas, J., Budde, W. and Panda-Jonas, S.: 1999, Ophthalmoscopic evaluation of the optic nerve
head, Survey of Ophthalmology 43(4), 193–320.
Jones, J. and Palmer, L.: 1987, An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex, Journal of Neurophysiology 58(6), 1233–1258.
Julesz, B.: 1981, Textons, the elements of texture perception, and their interactions, Nature
290(5802), 91–97.
Kalantidis, Y., Tolias, G., Avrithis, Y., Phinikettos, M., Spyrou, E., Mylonas, P. and Kollias, S.: 2011, Visual Image Retrieval and Localization, Multimedia Tools Appl. 51(2), 555–592. Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen,
R., Uusitalo, H., K¨alvi¨ainen, H. and Pietil¨a, J.: 2007, DIARETDB1 diabetic retinopathy database and evaluation protocol, In proc. of the Medical Image Understanding and Analysis, Aberystwyth, UK, pp. 61–65.
Kimme, C., B. D. S. J.: 1975, Find circles by an array of accumulators, Communications of the ACM 18(2), 120–122.
Kohler, T., Budai, A., Kraus, M., Odstrcilik, J., Michelson, G. and Hornegger, J.: 2013, Auto-matic no-reference quality assessment for retinal fundus images using vessel segmentation, 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS), pp. 95– 100.
Krizhevsky, A., Sutskever, I. and Hinton, G. E.: 2012, Imagenet classification with deep con-volutional neural networks, in F. Pereira, C. Burges, L. Bottou and K. Weinberger (eds), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., pp. 1097–1105. Kruizinga, P., P. N.: 1999, Non-linear operator for oriented texture. IEEE Transactions on
Image Processing, IEEE Transactions on Image Processing 8(10), 1395–1407.
Lalonde, M., Beaulieu, M. and Gagnon, L.: 2001, Fast and robust optic disc detection us-ing pyramidal decomposition and Hausdorff-based template matchus-ing, IEEE Trans. Med. Imaging 20(11), 1193–1200.
Lamme, V. A. F. and Roelfsema, P. R.: 2000, The distinct modes of vision offered by feedfor-ward and recurrent processing, Trends in Neurosciences 23, 571–579.
Lazebnik, S., Schmid, C. and Ponce, J.: 2005, A sparse texture representation using local affine regions, IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1265–1278. Lazebnik, S., Schmid, C. and Ponce, J.: 2006, Beyond Bags of Features: Spatial Pyramid
Match-ing for RecognizMatch-ing Natural Scene Categories, ProceedMatch-ings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 of CVPR ’06, IEEE Computer Society, pp. 2169–2178.
LeCun, Y., Bengio, Y. and Hinton, G.: 2015, Deep learning, Nature 521, 436–444.
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P.: 1998, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11), 2278–2324.
Li, F.-F. and Perona, P.: 2005, A Bayesian Hierarchical Model for Learning Natural Scene Categories, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2 of CVPR ’05, IEEE Computer Society, pp. 524–531.
Li, H. and Chutatape, O.: 2001, Automatic location of optic disk in retinal images, Image Processing, 2001. Proceedings. 2001 International Conference on, Vol. 2, pp. 837–840.
Lin, S., Singh, K., Jampel, H., Hodapp, E., Smith, S., Francis, B., Dueker, D., Fechtner, R., Samples, J., Schuman, J. and Minckler, D.: 2007, Optic nerve head and retinal nerve fiber layer analysis: a report by the American Academy of Ophthalmology, Ophthalmol-ogy 114(10), 1937–1949.
Lin, Y., Wenyin, L. and Jiang, C.: 2004, A structural approach to recognizing incomplete graphic objects, Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, Vol. 1, pp. 371–375.
Liu, C.-L., Nakashima, K., Sako, H. and Fujisawa, H.: 2003, Handwritten digit recognition: benchmarking of state-of-the-art techniques, Pattern Recognition 36(10), 2271–2285. Lowe, D. G.: 1999, Object Recognition from Local Scale-Invariant Features, Proceedings of the
International Conference on Computer Vision, Vol. 2 of ICCV ’99, pp. 1150–1157.
Lowe, D. G.: 2004, Distinctive Image Features from Scale-Invariant Keypoints, Int. J. Comput. Vision 60(2), 91–110.
Lowell, J., H. A. S. D. B. A. R. R. F. E. K. L.: 2004, Optic nerve head segmentation, IEEE Transactions on Medical Imaging 23(2), 256–264.
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E. and Kennedy, L.: 2004, Optic nerve head segmentation, IEEE Transactions on Medical Imaging 23(2), 256–264.
M. Abramoff, M. G. and Sonka, M.: 2010, Retinal imaging and image analysis, Biomedical Engineering 89, 169–208.
Mandic, D. and Goh, V. S. L.: 2009, Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models, Wiley Publishing.
Mendonc¸a, A. M., Sousa, A. V., Mendonc¸a, L. and Campilho, A. J. C.: 2013, Automatic local-ization of the optic disc by combining vascular and intensity information, Comp. Med. Imag. and Graph. 37(5-6), 409–417.
Mikolajczyk, K. and Schmid, C.: 2001, Indexing based on scale invariant interest points, Pro-ceedings of the 8th International Conference on Computer Vision, pp. 525–531.
Mikolajczyk, K. and Schmid, C.: 2005, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630.
Mittapalli, P. S. and Kande, G. B.: 2016, Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma, Biomedical Signal Processing and Control
Neocleous, A., Azzopardi, G., Schizas, C. and Petkov, N.: 2015, Filter-based approach for or-namentation detection and recognition in singing folk music, Computer Analysis of Images and Patterns, Vol. 9256 of Lecture Notes in Computer Science, Springer International Publish-ing, pp. 558–569.
Ojala, T., Pietik¨ainen, M. and M¨aenp¨a¨a, T.: 2002, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987.
Oliva, A. and Torralba, A.: 2001, Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope, International Journal of Computer Vision 42, 145–175.
Osareh, A., Mirmehdi, M., Thomas, B. and Markham, R.: 2002, Comparison of colour spaces for optic disc localisation in retinal images, 16th International Conference on Pattern Recogni-tion, 2002, Vol. 1, pp. 743–746.
Owen, C., Rudnicka, A., Mullen, R., Barman, S., Monekosso, D., Whincup, P., Ng, J. and Pa-terson, C.: 2009, Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program, Investigative Ophthal-mology and Visual Science 50(5), 2004–2010.
Papari, G., Petkov, N. and Campisi, P.: 2007, Artistic Edge and Corner Enhancing Smoothing, IEEE Transactions on Image Processing 16(10), 2449–2462.
Patton, N., Aslam, T., MacGillivray, T., Deary, I., Dhillon, B., Eikelboom, R., Yogesan, K. and Constable, I.: 2006, Retinal image analysis: Concepts, applications and potential, Progress in Retinal and Eye Research 25(1), 99–127.
Petkov, N.: 1995, Biologically motivated computationally intensive approaches to image pattern-recognition, Future Generation Computer Systems 11.
Petkov, N., K. P.: 1997, Computational models of visual neurons specialized in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells, Biological Cybernetics
76(2), 83–96.
Petkov, N. and Visser, W. T.: 2005, Modifications of center-surround, spot detection and dot-pattern selective operators, Institute of Mathematics and Computing Science, University of Groningen, The Netherlands (CS 2005-9-01), 1–4.
Petkov, N., W. M.: 2003, Suppression of contour perception by band-limited noise and its relation to non-classical receptive field inhibition, Biological Cybernetics 88(10), 236–246. Quigley, H. and Broman, A.: 2006, The number of people with glaucoma worldwide in
2010 and 2020. The British Journal of Ophthalmology, The British Journal of Ophthalmology
90(3), 262–267.
Ramdas, W., Wolfs, R., Hofman, A., de Jong, P., Vingerling, H. and Jansonius, N.: 2011, Hei-delberg Retina Tomograph (HRT3) in population-based epidemiology: Normative values and criteria for glaucomatous optic neuropathy, Ophthalmic Epidemiology 18(5), 198–210.
Rao, R. P., Olshausen, B. A. and Lewicki, M. S.: 2002, Probabilistic Models of the Brain: Per-ception and Neural Function, MIT Press, Cambridge, MA .
Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R. S., Guedes, C. and Cardoso, J. S.: 2012, Optical music recognition: state-of-the-art and open issues., International Journal of Multi-media Information Retrieval 1(3), 173–190.
Riesenhuber, M. and Poggio, T.: 1999, Hierarchical models of object recognition in cortex, Nature Neuroscience 2(11), 1019–1025.
Rolls, E. T. and Treves, A.: 1990, The relative advantages of sparse versus distributed encod-ing for associative neuronal networks in the brain, Network Computation in Neural Systems
1(4), 407–421.
Sami, E.-B. and Mriganka, S.: 2014, Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo, Nature Communications 5, 5689.
Shelhamer, E., Long, J. and Darrell, T.: 2017, Fully Convolutional Networks for Semantic Segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651.
Shi, C., Guo, J., Azzopardi, G., Meijer, J. M., Jonkman, M. F. and Petkov, N.: 2015a, Auto-matic differentiation of u- and n-serrated patterns in direct immunofluorescence images, Computer Analysis of Images and Patterns 2015, Vol. 9256 of Lecture Notes in Computer Science, Springer International Publishing, pp. 513–521.
Shi, C., Guo, J., Azzopardi, G., Meijer, J. M., Jonkman, M. F. and Petkov, N.: 2015b, Auto-matic differentiation of u- and n-serrated patterns in direct immunofluorescence images, Computer Analysis of Images and Patterns 2015, Vol. 9256 of Lecture Notes in Computer Science, Lecture Notes in Computer Science, pp. 513–521.
Sinthanayothin, C., Boyce, J., Cook, H. and Williamson, T.: 1999, Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images, The British Journal of Ophthalmology 8(83), 902–910.
Sivaswamy, J., Krishnadas, S., Datt Joshi, G., Jain, M. and Ujjwaft Syed Tabish, A.: 2014, Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 53–56.
Springelkamp, H., H ¨ohn, R., Mishra, A., Hysi, P., Khor, C.-C., Loomis, S., Bailey, J., Gibson, J., Thorleifsson, G., Janssen, S., Luo, X., Ramdas, W., Vithana, E., Nongpiur, M., Montgomery, G., Xu, L., Mountain, J., Gharahkhani, P., Lu, Y., Amin, N., Karssen, L., Sim, K.-S., van Leeuwen, E., Iglesias, A., Verhoeven, V., Hauser, M., Loon, S.-C., Despriet, D., Nag, A., Venturini, C., Sanfilippo, P., Schillert, A., Kang, J., Landers, J., Jonasson, F., Cree, A., van Koolwijk, L., Rivadeneira, F., Souzeau, E., Jonsson, V., Menon, G., Weinreb, R., de Jong, P., Oostra, B., Uitterlinden, A., Hofman, A., Ennis, S., Thorsteinsdottir, U., Burdon, K., Spector, T., Mirshahi, A., Saw, S.-M., Vingerling, J., Teo, Y.-Y., Haines, J., Wolfs, R., Lemij, H., Tai, E.-S., Jansonius, N., Jonas, J., Cheng, C.-Y., Aung, T., Viswanathan, A., Klaver, C., Craig,
J., Macgregor, S., Mackey, D., Lotery, A., Stefansson, K., Bergen, A., Young, T., Wiggs, J., Pfeiffer, N., Wong, T.-Y., Pasquale, L., Hewitt, A., van Duijn, C., Hammond, C. and Blue Mountains Eye Study-GWAS group: 2014, Meta-analysis of genome-wide association studies identifies novel loci that influence cupping and the glaucomatous process, Nature Communications 5:4883.
Springelkamp, H., Iglesias, A. I., Mishra, A., H ¨ohn, R., Wojciechowski, R., Khawaja, A. P., Nag, A., Wang, Y. X., Wang, J. J. and MacGregor, . S.: 2017, New insights into the genetics of primary open-angle glaucoma based on meta-analyses of intraocular pressure and optic disc characteristics, Human Molecular Genetics 26(2), 438–453.
Springelkamp, H., Wolfs, R. C., Ramdas, W. D., Hofman, A., Vingerling, J. R., Klaver, C. C. and Jansonius, N. M.: 2017, Incidence of glaucomatous visual field loss after two decades of follow-up: the Rotterdam Study, European Journal of Epidemiology .
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M. and van Ginneken, B.: 2004, Ridge-based vessel segmentation in color images of the retina, IEEE Transactions on Medical Imaging
23(4), 501–509.
Strisciuglio, N., Azzopardi, G., Vento, M. and Petkov, N.: 2015, Multiscale blood vessel delin-eation using B-COSFIRE filters, Computer Analysis of Images and Patterns 2015, Vol. 9257 of Lecture Notes in Computer Science, Springer International Publishing, pp. 300–312.
Strisciuglio, N., Azzopardi, G., Vento, M. and Petkov, N.: 2016, Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters, Machine Vision and Applications 27(8), 1137–1149.
Strisciuglio, N., Vento, M., Azzopardi, G. and Petkov, N.: 2016, Unsupervised delineation of the vessel tree in retinal fundus images, Computational Vision and Medical Image Processing: VIPIMAGE 2015, Vol. 1, CRC Press/Balkema, Taylor and Francis Group, pp. 149–155. Tang, P., Hui, S. C. and Fu, C.-W.: 2013, Online chemical symbol recognition for handwritten
chemical expression recognition, Computer and Information Science (ICIS), 2013 IEEE/ACIS 12th International Conference on, IEEE, pp. 535–540.
Tham, Y.-C., Li, Xiang, W., Y., T., Quigley, H. A., Aung, Tin, C. and Ching-Yu: 2014, Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040, Ophthalmology
121(11), 2081–2090.
Tsai, C.-L., Stewart, C. V., Tanenbaum, H. L. and Roysam, B.: 2004, Model-Based Method for Improving the Accuracy and Repeatability of Estimating Vascular Bifurcations and Crossovers From Retinal Fundus Images, IEEE Transactions on Information Technology in Biomedicine 8(2), 122–130.
Tso, M. and Jampol, L.: 1982, Path-physiology of hypertensive retinopathy, Opthalmology
Vaahtoranta-Lehtonen, H., Tuulonen, A., Aronen, P., Sintonen, H., Suoranta, L., Kovanen, N., Linna, M., L¨a¨ar¨a, E. and Malmivaara, A.: 2007, Cost Effectiveness and Cost Utility of an Organized Screening Programme for Glaucoma, Acta Ophthalmologica 85(5), 508–518. Valveny, E., Delalandre, M., Raveaux, R. and Lamiroy, B.: 2011, Report on the Symbol
Recog-nition and Spotting Contest, Workshop on Graphics RecogRecog-nition (GREC 2011), Vol. 7423, Springer, pp. 198–207.
Valveny, E., Dosch, P., Winstanley, A., Zhou, Y., Yang, S., Yan, L., Wenyin, L., Elliman, D., Delalandre, M., Trupin, E., Adam, S. and Ogier, J.-M.: 2007, A General Framework for the Evaluation of Symbol Recognition Methods, Int. J. Doc. Anal. Recognit. 9(1), 59–74.
Viola, P. and Michael, J. J.: 2004, Robust Real-Time Face Detection, Int. J. Comput. Vision
57(2), 137–154.
Wade, N. J.: 2010, Pioneers of Eye Movement Research, i-Perception 1(2), 33–68.
Walter, T. and Klein, J.-C.: 2001, Segmentation of color fundus images of the human retina: de-tection of the optic disc and the vascular tree using morphological techniques, Proceedings of the Second International Symposium on Medical Data Analysis, ISMDA ’01, Springer-Verlag, pp. 282–287.
Weinreb, R. and Greve, E.: 2004, Glaucoma Diagnosis, Structure and Function The Hague:
Kugler Publications.
Wolfs, R. C. W., Borger, P. H., Ramrattan, R. S., Klaver, C. C. W., Hulsman, C. A. A., Hofman, A., Vingerling, J. R., Hitchings, R. A. and de Jong, P. T. V. M.: 2000, Changing Views on Open-Angle Glaucoma: Definitions and Prevalences–The Rotterdam Study, Investigative Ophthalmology & Visual Science 41(11), 3309–3321.
Wong, D. W. K., Liu, J., Tan, N. M., Fengshou, Y., Cheung, C., Baskaran, M., Aung, T. and Wong, T. Y.: 2012, An ensembling approach for optic cup detection based on spatial heuris-tic analysis in retinal fundus images, 2012 Annual International Conference of the IEEE Engi-neering in Medicine and Biology Society, pp. 1426–1429.
Wong, D. W. K., Liu, J., Tan, N. M., Yin, F., Lee, B. H., Tham, Y. C., lui Cheung, C. Y. and Wong, T. Y.: 2012, Detecting the optic cup excavation in retinal fundus images by automatic detec-tion of vessel kinking, Proceedings of the 21st Internadetec-tional Conference on Pattern Recognidetec-tion, IEEE Computer Society, pp. 73–76.
Wong, T. Y. and Bressler, N. M.: 2016, Artificial intelligence with deep learning technology looks into diabetic retinopathy screening, JAMA 316(22), 2366–2367.
Xu, J., Ishikawa, H., Wollstein, G., Bilonick, R. A., Sung, K. R., Kagemann, L., Townsend, K. A. and Schuman, J. S.: 2008, Automated assessment of the optic nerve head on stereodisc photographs, Investigative Ophthalmology and Visual Science 49(6), 2512–2517.
Yajie, Y., Zhang, W. and Wenyin, L.: 2007, A New Syntactic Approach to Graphic Symbol Recognition, Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Con-ference on, Vol. 1, pp. 516–520.
Yang, S.: 2014, Spectra of shape contexts: An application to symbol recognition, Pattern Recog-nition 47(5), 1891–1903.
Yin, F., Liu, J., Wong, D. W. K., Tan, N. M., Cheung, C., Baskaran, M., Aung, T. and Wong, T. Y.: 2012, Automated segmentation of optic disc and optic cup in fundus images for glau-coma diagnosis, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6.
Zanibbi, R., Blostein, D. and Cordy, J. R.: 2002, Recognizing Mathematical Expressions Us-ing Tree Transformation., IEEE Transactions on Pattern Analysis and Machine Intelligence
24(11), 1455–1467.
Zhang, J., Lazebnik, S. and Schmid, C.: 2007, Local features and kernels for classification of texture and object categories: a comprehensive study, International Journal of Computer Vision 73(2), 213–238.
Zhang, W., Wenyin, L. and Zhang, K.: 2006, Symbol Recognition with Kernel Density Match-ing, Pattern Analysis and Machine Intelligence, IEEE Transactions on 28(12), 2020–2024. Zhang, Y., Tian, T., Tian, J., Gong, J. and Ming, D.: 2014, A novel biologically inspired local
