(45-5) 11 * << * >> * Russian * English * Content * All Issues

Automated combination of optical coherence tomography images and fundus images
A.D. Fida 1, A.V. Gaidel 1,2, N.S. Demin 1,2, N.Yu. Ilyasova 1,2, E.A. Zamytskiy 3

1 Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,
2 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151,
3 Samara Regional Clinical Ophthalmological Hospital named after T.I. Eroshevsky,
443066, Samara, Russia, Zaporozhskaya 26

 PDF, 4348 kB

DOI: 10.18287/2412-6179-CO-892

Pages: 721-727.

Full text of article: Russian language.

Abstract:
We discuss approaches to combining multimodal multidimensional images, namely, three-dimensional optical coherence tomography (OCT) data and two-dimensional color images of the fundus. Registration of these two modalities can help to adjust the position of the obtained OCT images on the retina. Some existing approaches to matching fundus images are based on finding key points that are considered invariant to affine transformations and are common to the two images. However, errors in the identification of such points can lead to registration errors. There are also methods for iterative adjustment of conversion parameters, but they are based on some manual settings. In this paper, we propose a method based on a full or partial search of possible combinations of the OCT image transformation to find the best approximation of the true transformation. The best approximation is determined using a measure of comparison of preprocessed image pixels. Further, the obtained transformations are compared with the available true transformations to assess the quality of the algorithm. The structure of the work includes: pre-processing of OCT and fundus images with the extraction of blood vessels, random search or grid search over possible transformation parameters (shift, rotation and scaling), and evaluation of the quality of the algorithm.

Keywords:
image processing, optical coherence tomography, fundus, image matching.

Citation:
Fida AD, Gaidel AV, Demin NS, Ilyasova NY, Zamytskiy EA. Automated combination of optical coherence tomography images and fundus images. Computer Optics 2021; 45(5): 721-727. DOI: 10.18287/2412-6179-CO-892.

Acknowledgements:
This work was financially supported by the Russian Foundation for Basic Research under grant # 19-29-01135 and the RF Ministry of Science and Higher Education under a government project of the FSRC “Crystallography and Photonics” RAS.

References:
  1. Dedov II, Shestakova MV, Galstyan GP. The prevalence of type 2 diabetes in the adult population of Russia (NATION study) [In Russian]. Diabetes Mellitus 2016; 19(2): 104-112.
  2. Amirov AN, Abdulaeva EA, Minkhuzina EL Diabetic macular edema. epidemiology, pathogenesis, diagnosis, clinical features, treatment [In Russian]. Kazan Medical Journal 2015; 96(1): 70-74.
  3. Tsujimoto T, Kajio H. Four-year screening interval and vision-threatening retinopathy in type 2 diabetes patients with good glycemic control. Mayo Clinic Proceedings 2021; 96(2): 322-331.
  4. Tan GS, Cheung N, Simo R. Diabetic macular edema. Lancet Diabetes Endocrinol 2017; 5: 143-155.
  5. Hurley B. Therapeutic revolution in the management of diabetic retinopathy. Can J Ophthalmol 2017, 52(1): 1-2.
  6. Barry GP, Tauber KA, Greenberg S, Lajoie J, Afroze F, Oechsner H, Finucane E, Binenbaum G. A comparison of respiratory outcomes after treating retinopathy of prematurity with laser photocoagulation or intravitreal bevacizumab. Ophthalmol Retina 2020, 4(12): 1202-1208.
  7. Kotsur TV, Izmailov AS. The effectiveness of laser coagulation in the macula and high-density microphotocoagulation in the treatment of diabetic maculopathy. Ophthalmological Statements 2016, 9(4): 43-45.
  8. Zamytsky EA, Zolotarev AV, Karlova EV, Zamytsky PA. Analysis of the coagulates intensity in laser treatment of diabetic macular edema in a NAVILAS robotic laser system. Saratov Journal of Medical Scientific Research 2017, 13(2): 375-378.
  9. Ober MD. Time required for navigated macular laser photocoagulation treatment with the Navilas®. Graefes Arch Clin Exp Ophthalmol 2013; 251(4): 1049-1053.
  10. Vergmann AS, Nguyen TT, Torp TL, Kawasaki R, Wong TY, Peto T, Grauslund J. Efficacy and side effects of individualized panretinal photocoagulation. Ophthalmol Retina 2020; 4(6): 642-644.
  11. Ilyasova NYu, Shirokanev AS, Kupriyanov AV, Paringer RA. Technology of intellectual feature selection for a system of automatic formation of a coagulate plan on retina. Computer Optics 2019; 43(2): 304-315. DOI: 10.18287/2412-6179-2019-43-2-304-315.
  12. Ilyasova NYu, Paringer RA, Kupriyanov AV. Regions of interest in a fundus image selection technique using the discriminative analysis methods. In Book: Chmielewski LJ, Datta A, Kozera R, Wojciechowski K, eds. Computer vision and graphics (ICCVG 2016). Cham: Springer; 2016: 408-417. DOI: 10.1007/978-3-319-46418-3_36.
  13. Ilyasova NYu, Shirokanev AS, Paringer RA, Kupriyanov AV. A modified technique for smart textural feature selection to extract retinal regions of interest using image pre-processing. J Phys Conf Ser 2018; 1096: 012095. DOI: 10.1088/1742-6596/1096/1/012095.
  14. Yoo TK, Choi JY, Seo JG. The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Med Biol Eng Comput 2019; 57: 677-687.
  15. Radim K, Pavel T. Registration of 3D retinal optical coherence tomography data and 2D fundus images. In Book: Fischer B, Dawant BM, Lorenz C, eds. Biomedical Image Registration. Berlin, Heidelberg: Springer-Verlag; 2010: 72-82.
  16. Zeinab G, Jamshid S, Amin S, Emad F. An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors. EURASIP J Image Video Process 2013; 1(25): 1-16.
  17. Golabbakhsh M, Rabbani H. Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration. IET Image Process 2013; 7(8): 768-776.
  18. Chojnacki W, Szpak ZL, Wadenbäck M. The equivalence of two definitions of compatible homography matrices. Patt Recogn Lett 2020; 135: 38-43.
  19. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comp Vis 2004; 60: 91-110.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20