Development of a Method of Feature Space Formation for Assessment of Choroidea Condition from Retinal Angio-OCT Images

Nataly Yu. Ilyasova (Login required)
IPSI, NRC “Kurchatov Institute”, Samara, Russian Federation
Samara National Research University, Russian Federation

Ravil T. Samigullin
Samara National Research University, Russian Federation

Nikita S. Demin
IPSI, NRC “Kurchatov Institute”, Samara, Russian Federation
Samara National Research University, Russian Federation


Paper #9118 received 21 Jun 2024; revised manuscript received 19 Sep 2024; accepted for publication 20 Sep 2024; published online 29 Sep 2024.

DOI: 10.18287/JBPE24.10.030306

Abstract

This paper presents a technique for selecting regions of interest in retinal angio-OCT images to quantitatively analyze choroidea parameters in order to detect ocular diseases. The significance of this research lies in the fact that choroideas are an important part of the eye responsible for nourishing the retina and maintaining its normal function. Disruptions in the choriodea lead to various eye conditions, including degenerative retina diseases and glaucoma. A method for assessing choriodeal conditions has been developed based on identifying areas without vascular signal in retinal angiographic OCT images. Additional indicators for estimating choriodean status have been proposed. Comparative analysis of parameters between norm and pathological classes was conducted. The results of this work may be useful for ophthalmologists and contribute to improved diagnosis and treatment for eye conditions.

Keywords

biomedical images; optical coherence tomography images; threshold processing; choroid; quantitative features; quantitative analysis

Full Text:

PDF

References


1. R. Agrawal, P. Gupta, K. A. Tan, C. M. G. Cheung, T. Y. Wong, and C. Y. Cheng, “Choroidal vascularity index as a measure of vascular status of the choroid: Measurements in healthy eyes from a population-based study,” Scientific Reports 6, 21090 (2016).

2. L. I. Balashevich, M. V. Gatsu, E. M. Kasimov, and N. G. Iskanderova, “Current views on the etiology and pathogenesis of central serous chorioretinopathy,” Ophthalmosurgery 2, 63-67 (2007). [in Russian]

3. A. Nurmukhamedova, A. R. Utaralina, N. A. Khvaleva, and A. S. Dobrynin, “Vogt-Koyanagi-Harada disease,” Proceedings of Student Research, G. Yu. Gulyaev (Ed.), Penza, Russian Federation, 201-203 (2021). ISBN: 978-5-00159-742-1. [in Russian]

4. E. A. Abdulaeva, “Polypoidal choroidal vasculopathy,” Kazan Medical Journal 98(3), 403-409 (2017). [in Russian]

5. S. E. Avetisov, V. P. Yerichev, M. V. Budzinskaya, and M. A. Karpilova, “Age-related macular degeneration and ocular hypertension,” National Journal Glaucoma (1), 62-67 (2013). [in Russian]

6. K. Breher, L. Terry, T. Bower, and S. Wahl, “Choroidal Biomarkers: A Repeatability and Topographical Comparison of Choroidal Thickness and Choroidal Vascularity Index in Healthy Eyes,” Translational Vision Science & Technology 9(11), 8 (2020).

7. N. Yu. Ilyasova, N. S. Demin, A. S. Shirokanev, A. V. Kupriyanov, and E. A. Zamytsky, “Method for selection macular edema region using optical coherence tomography data,” Computer Optics 44(2), 250-258 (2020).

8. N. Yu. Ilyasova, D. V. Kirsh, and N. S. Demin, “Decision-making support system for the personalisation of retinal laser treatment in diabetic retinopathy,” Computer Optics 46(5), 774-782 (2022).

9. R. A. Paringer, A. V. Mukhin, N. Yu. Ilyasova, and N. S. Demin, “Neural networks application for semantic segmentation of fundus,” Computer Optics 46(4), 596-602 (2022). [in Russian]

10. H. Raja, M. U. Akram, S. G. Khawaja, M. Arslan, A. Ramzan, and N. Nazir, “Data on OCT and fundus images for the detection of glaucoma,” Data in Brief 29, 105342 (2020).

11. G. An, K. Omodaka, K. Hashimoto, S. Tsuda, Y. Shiga, N. Takada, T. Kikawa, H. Yokota, M. Akiba, and T. Nakazawa, “Glaucoma diagnosis with machine learning based on optical coherence tomography and colour fundus images,” Journal of Healthcare Engineering 2019, 4061313 (2019).

12. B. Hassan, R. Ahmed, B. Li, O. Hassan, and T. Hassan, “Automated retinal edema detection from fundus and optical coherence tomography scans,” in 2019 5th International Conference on Control, Automation and Robotics (ICCAR), IEEE, 325-330 (2019).

13. A. Uji, S. Balasubramanian, J. Lei, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Choriocapillaris imaging using multiple en face optical coherence tomography angiography image averaging,” JAMA Ophthalmology 135(11), 1197-1204 (2017).

14. Z. Chu, H. Zhou, Y. Cheng, Q. Zhang, and R. K. Wang, “Improving visualisation and quantitative assessment of choriocapillaris with swept source OCTA through registration and averaging applicable to clinical systems,” Scientific Reports 8(1), 16826 (2018).

15. R. F. Spaide, “Choriocapillaris flow features follow a power law distribution: implications for characterisation and mechanisms of disease progression,” American Journal of Ophthalmology 170, 58-67 (2016).

16. K. A. Halavataya, K. V. Kozadaev, and V. S. Sadau, “Adjusting videoendoscopic 3D reconstruction results using tomographic data,” Computer Optics 46(2), 246-251 (2022).

17. M. A. Burnasheva, A. N. Kulikov, and D. S. Maltsev, “Artifact-free evaluation of choriocapillaris perfusion in central serous chorioretinopathy,” Vision 5(1), 3 (2020).

18. Z. Chu, Q. Zhang, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Guidelines for imaging the choriocapillaris using OCT angiography,” American Journal of Ophthalmology 222, 92-101 (2021).

19. K. Takayama, H. Kaneko, Y. Ito, K. Kataoka, T. Iwase, T. Yasuma, T. Matsuura, T. Tsunekawa, H. Shimizu, A. Suzumura, E. Ra, T. Akahori, and H. Terasaki, “Novel classification of early-stage systemic hypertensive changes in human retina based on OCTA measurement of choriocapillaris,” Scientific Reports 8(1), 15163 (2018).

20. F. F. Conti, V. L. Qin, E. B. Rodrigues, S. Sharma, A. V. Rachitskaya, J. P. Ehlers, and R. P. Singh, “Choriocapillaris and retinal vascular plexus density of diabetic eyes using split-spectrum amplitude decorrelation spectral-domain optical coherence tomography angiography,” British Journal of Ophthalmology 103(4), 452-456 (2019).

21. O. Loria, L. Kodjikian, P. Denis, C. Vartin, S. Dimassi, L. Gervolino, A. Maignan, R. Kermarrec, C. Chambard, P. Pradat, and T. Mathis, “Quantitative analysis of choriocapillaris alterations in swept-source optical coherence tomography angiography in diabetic patients,” Retina 41(9), 1809-1818 (2021).

22. D. S. Maltsev, A. V. Fomin, A. N. Kulikov, and A. S. Vasiliev, “Evaluation of the choriocapillaris status using high-speed spectral optical coherence tomography with angiography function and image averaging technology,” Vestnik Ophthalmologii 137(3), 76-84 (2021). [in Russian]

23. Z. Chu, Y. Cheng, Q. Zhang, H. Zhou, Y. Dai, Y. Shi, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Quantification of Choriocapillaris with Phansalkar Local Thresholding: Pitfalls to Avoid,” American Journal of Ophthalmology, 213, 161-176 (2020).

24. А. А. Kolchev, D. V. Pasynkov, I. A. Egoshin, I. V. Kliouchkin, and О. О. Pasynkova, “Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area,” Computer Optics 48(1), 157-165 (2024).

25. N. Y. Ilyasova, A. V. Kupriyanov, and A. G. Khramov, Information technologies of image analysis in the tasks of medical diagnostics, A. S. Bugaev (Ed.), Radio and Communication, Moscow (2012). ISBN: 5-89776-014-4. [in Russian]






© 2014-2025 Authors
Public Media Certificate (RUS
). 12+