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2021 | OriginalPaper | Chapter

Automation of the Detection of Pathological Changes in the Morphometric Characteristics of the Human Eye Fundus Based on the Data of Optical Coherence Tomography Angiography

Authors : Igor Gurevich, Maria Budzinskaya, Vera Yashina, Adil Tleubaev, Vladislav Pavlov, Denis Petrachkov

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

This paper presents the results of the joint work of image analysis specialists and ophthalmologists on the task of analyzing images obtained by the method of optical coherence tomography angiography. A method was developed to automate the detection of pathological changes in the morphometric characteristics of the fundus. The solution of the image recognition problem assumes the presence of certain image representations, the presence of effective recognition algorithms, and the compliance of the used image representations with the requirements of the recognition algorithms for the source data. To reduce images to a form that is easy to recognize we considered sets of features that met all the necessary requirements of specialists. Chosen feature model was implemented to the problem of classification of images of patients with and without pathologies. The developed method makes it possible to classify pathological changes in the vascular bed of the human eye with high accuracy.

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Cheung, N., Mitchell, P., Wong, T.Y.: Diabetic retinopathy. Lancet 376, 124–146 (2010). https://doi.org/10.1016/S0140-6736(09)62124-3CrossRef

Chu, Z., et al.: Quantitative assessment of the retinal microvasculature using optical coherence tomography angiography. J. Biomed. Opt. 21(6), 066008 (2016)

Dedov, I., Shestakova, M., Vikulova, O.: Epidemiology of diabetes mellitus in Russian Federation: clinical and statistical report according to the federal diabetes registry. Diabetes Mellitus 20(1), 13–41 (2017)CrossRef

Fong, D.S., Gottlieb, J., Ferris, F.L., Iii, Klein, R.: Understanding the value of diabetic retinopathy screening. Arch. Ophthalmol. 119(5), 758–760 (2001). https://doi.org/10.1001/archopht.15.758

Gildea, D.: The diagnostic value of optical coherence tomography angiography indiabetic retinopathy: a systematic review. Int. Ophthalmol. 39(10), 2413–2433 (2019). https://doi.org/10.1007/s10792-018-1034-8

Gurevich, I.B., Yashina, V.V., Ablameyko, S.V., et al.: Development and experimental investigation of mathematical methods for automating the diagnostics and analysis of ophthalmological images. Pattern Recognit. Image Anal. 28, 612–636 (2018)CrossRef

Gurevich, I.B., Harazishvili, D.V., Salvetti, O., Trykova, A.A., Vorob’ev, I.A.: Elements of the information technology of cytological specimens analysis: taxonomy and factor analysis. Pattern Recognit. Image Anal. Adv. Math. Theory Appl. MAIK Nauka/Interperiodica 16(1), 114–116 (2006)

Gurevich, I.B., Yashina, V.V., Fedorov, A.A., Nedzved, A.M., Ospanov, A.M.: Development, investigation, and software implementation of a new mathematical method for automated identification of the lipid layer state by the images of eyelid intermarginal space. Pattern Recognit. Image Anal. Adv. Math. Theory Appl. 27(3), 53–549 (2017a)

Gurevich, I.B., Yashina, V.V., Fedorov, A.A., Nedzved, A.M.. Tleubaev. A.T.: Development, investigation, and software implementation of a new mathematical method for automatizing analysis of corneal endothelium images. Pattern Recognit. Image Anal. Adv. Math. Theory Appl. 27(3), 550–559 (2017b)

Gurevich, I.B., Zhuravlev, Yu.I., Myagkov, A.A., Trusova, Yu.O., Yashina, V.V.: On basic problems of image recognition in neurosciences and heuristic methods for their solution. Pattern Recognit. Image Anal. Adv. Math. Theory Appl. 25(1), 132–160 (2015)

Hirano, T., et al.: Vitreoretinal interface slab in OCT angiography for detecting diabetic retinal neovascularization. Ophthalmol. Retina pii, S2468-6530(20)30017-8 (2020). https://doi.org/10.1016/j.oret.2020.01.004

Hirano, T., Kitahara, J., Toriyama, Y., et al.: Quantifying vascular density and morphology using different swept-source optical coherence tomography angiographic scan patterns in diabetic retinopathy. Br. J. Ophthalmol. 103, 216–221 (2019). https://doi.org/10.1136/bjophthalmol-2018-311942CrossRef

João, V.B., et al.: Retinal vessel segmentation using the 2-D Morlet wavelet and supervised classification. IEEE Trans. Med. Imag. 25(9), 1214–1222 (2006)

Nedzved, A., Nedzved, O., Glinsky, A., Karapetian, G., Gurevich, I.B., Yashina, V.V.: Detection of dynamical properties of flow in an eye vessels by video sequences analysis. In: Proceedings of the International Conference on Information and Digital Technologies, Zilina, Slovakia, 5–7 July, pp. 280–285. The Institute of Electrical and Electronics Engineers, Inc. (2017)

Olvera-Barrios, A., Heeren, T.F., Balaskas, K., et al.: Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images. Br. J. Ophthalmol. (2020). https://doi.org/10.1136/bjophthalmol-2019-315394

Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognit. 33(2), 225–236 (2000)

Sinclair, S.H., Delvecchio, C.: The internist’s role in managing diabetic retinopathy: screening for early detection. Cleve. Clin. J. Med. 71(2), 151–159 (2004). https://doi.org/10.3949/ccjm.71.2.151CrossRef

Shapiro, L.G., Stockman, G.C.: Computer Vision, pp. 137, 150. Prentice Hall, Englewood (2001)

Spaide, R.F., Fujimoto, J.G., Waheed, N.K., Sadda, S.R., Staurenghi, G.: Optical coherence tomography angiography. Prog Retin Eye Res. 64, 1–55 (2018)CrossRef

Spaide, R.F.: Volume-rendered optical coherence tomography of diabetic retinopathy pilot study. Am. J. Ophthalmol. 160, 1200–1210 (2015)CrossRef

Vermeer, K.A., Vos, F.M., Lemij, H.G., Vossepoel, A.M.: A model based method for retinal blood vessel detection. Comput. Biol. Med. 34, 209–219 (2004)

Vujosevic, S., et al.: Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol. 8(4), 337–347 (2020). https://doi.org/10.1016/S2213-8587(19)30411-5CrossRef

Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 40(4), 438–445 (2010)

Title: Automation of the Detection of Pathological Changes in the Morphometric Characteristics of the Human Eye Fundus Based on the Data of Optical Coherence Tomography Angiography
Authors: Igor Gurevich
Maria Budzinskaya
Vera Yashina
Adil Tleubaev
Vladislav Pavlov
Denis Petrachkov
Publisher: Springer International Publishing
Book: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-68821-9_24

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