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The Automated Expert Support System for Optic Nerve Head Morphological Description

https://doi.org/10.18008/1816-5095-2020-4-817-823

Abstract

Purpose. To develop the automated expert support system for optic nerve head morphological description in normal conditions and in pathology.

Methods. The proposed expert support system is based on the integration algorithm of luminance samples along the diagonal, it allows to detect the optic nerve head border. On the basis of this algorithm the method for solving the following tasks of fundus image processing have been proposed: detecting of the optic nerve head border, method of the morphological description of the optic nerve head boundary, method of the determining the value of the disk excavation. An experimental study of the parameters effect on the effectiveness of the optic nerve head detecting method was made.

Results. The effectiveness assessment of the proposed border detection algorithm on the optic nerve head model has showed that the amount of overlap averaged 0.985, which indicates high quality. It was found that the algorithm for estimating the diameter of the single-sided optic nerve head image is sufficiently resistant to changes in such parameters as the influence of the noise level in the scene and the offset of the strobe center coordinates of the samples accumulation from the image center coordinates. Evaluation of the efficiency of the optic nerve head borders morphological description has showed that the value of the first-order derivative of the result of accumulation of luminance readings diagonally for images of optic nerve head with blurred boundaries is 2 times smaller than for images of optic nerve head with clear boundaries. The effectiveness of the method of selecting the border for assessment the disk excavation size was examined. It was obtained that the error in estimating the magnitude of excavation amounted to an average of 8.43 %.

Conclusions. Тhe presented expert support system allows to automate the process of optic disk morphological description, in particular, such parameters as the state of the border and the size of the disc excavation. This method can be used to create medical expert systems and software for fundus images processing. 

About the Authors

E. G. Tanaeva
Volga State University of Technology; Republican G.I. Grigoriev Ophthalmologic Hospital
Russian Federation

postgraduate, Lenin sq., 3, Yoshkar-Ola, Republic of Mari El, 424000;

phthalmologist, Proletarskaya str., 68А, Yoshkar-Ola, Republic of Mari EL, 424000



R. G. Khafizov
Volga State University of Technology
Russian Federation

Dr. Sc., Tech., Professor, 

Lenin sq., 3, Yoshkar-Ola, Republic of Mari El, 424000



References

1. Katsnelson L.A., Lysenko V.S., Balishanskaya T.I. Clinical Atlas of Eye Fundus Pathology. Moscow; 1997:152 (In Russ.).

2. Lowell J., Hunter A., Steel D., Basu A., Ryder R., Fletcher E., Kennedy L. Optic nerve head segmentation. IEEE Trans. Medical Imaging. 2004;23(2):256–264. DOI: 10.1109/TMI.2003.823261

3. Li H.Q., Chutatape O. Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 2004;51(2):246–254. DOI: 10.1109/TBME.2003.820400

4. Rangayyan M., Xiaolu Z., Fábio J.A., Anna L.E. Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis Rangaraj. Journal of Digital Imaging. 2010;23(4):438–453.

5. Sinthanayothin C., Boyce J.F., Cook H.L., Williamson, T.H. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 1999;83:902–910.

6. Yu H., Barriga E.S., Agurto C., Echegaray S., Pattichis M.S., Bauman W., Soliz P. Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans. Inf. Technol. Biomed. 2012;16(4):644– 657. DOI: 10.1109/TITB.2012.2198668

7. Aquino M.E., Gegundez-Arias. D. M. Detecting the optic disc boundary in digital fundus images using morphological edge detection and feature extraction techniques, IEEE Trans. Med. Imag. 2010;29(11):1860–1869. DOI: 10.1109/ TMI.2010.2053042

8. Hoover A., Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imag. 2003;22(8):951–958. DOI: 10.1109/TMI.2003.815900

9. Khafizov R.G., Tanaeva E.G. Тhe method of the optic disc detection of fundus images. Medical equipment = Meditsinskaia Tekhnika. 2018;2:19–22 (In Russ.).

10. Khafizov R.G., Tanaeva E.G. The morphological description automation of the optic nerve head boundary in digital fundus images. Ophthalmology in Russia = Oftal’mologiya. 2018;15(3):325–329 (In Russ.) DOI: 10.18008/1816-5095-2018-3-325-329

11. Lebedev O.I., Kalizhnikova E.A., Yarovskiy A.E. Top-list of management of patients with glaucoma: ophthalmoscopy. National Journal Glaucoma = Natsional’nyi zhurnal Glaucoma. 2014;1:35–44 (In Russ.).

12. Furman Ya.A., Kravetskii A.V., Peredreev A.K., Rozhentsov A.A., Khafizov R.G., Egoshina I.L., Leukhin A.N. Introduction to the contour analysis: application to image and signal processing. Moscow: Fizmatlit; 2003:592 (In Russ.).

13. Khafizov R.G., Tanaeva E.G., Egoshin M.A. The method of the optic disc detection and morphological description automation in digital fundus images. System analysis and management in biomedical systems = Sistemnyj analiz i upravlenie v biomedicinskih sistemah 2018;17(1):122–126 (In Russ.).


Review

For citations:


Tanaeva E.G., Khafizov R.G. The Automated Expert Support System for Optic Nerve Head Morphological Description. Ophthalmology in Russia. 2020;17(4):817-823. (In Russ.) https://doi.org/10.18008/1816-5095-2020-4-817-823

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ISSN 1816-5095 (Print)
ISSN 2500-0845 (Online)