SEGMENTATION OF MULTIGRADATION IMAGES BASED ON SPATIAL CONNECTIVITY FEATURES

Leonid Timchenko; Natalia Kokriatskaya; Volodymyr Tverdomed; Oleksandr Stetsenko; Valentina Kaplun; Oleg K. Kolesnytskyj; Oleksandr Reshetnik; Saule Smailova; Ulzhalgas Zhunissova

doi:10.35784/iapgos.5352

Open full text

PDF

Published: Sep 30, 2023

DOI: https://doi.org/10.35784/iapgos.5352

DOI

https://doi.org/10.35784/iapgos.5352

Authors

Leonid Timchenko

tumchenko_li@gsuite.duit.edu.ua

State University of Infrastructure and Technology, Artificial Intelligence Systems and Telecommunication Technologies Department

https://orcid.org/0000-0001-5056-5913

Natalia Kokriatskaya

kokryatska_ni@gsuite.duit.edu.ua

State University of Infrastructure and Technology, Artificial Intelligence Systems and Telecommunication Technologies Department

https://orcid.org/0000-0003-0090-3886

Volodymyr Tverdomed

tverdomed@gsuite.duit.edu.ua

1State University of Infrastructure and Technology, Artificial Intelligence Systems and Telecommunication Technologies Department, 2Kyiv Institute of Railway Transport

http://orcid.org/0000-0002-0695-1304

Oleksandr Stetsenko

stetsenko_oo@gsuite.duit.edu.ua

State University of Infrastructure and Technology, Artificial Intelligence Systems and Telecommunication Technologies Department

http://orcid.org/0000-0001-8359-0218

Valentina Kaplun

valentina.kaplun@vntu.edu.ua

Vinnytsia National Technical University

http://orcid.org/0000-0003-4353-3694

Oleg K. Kolesnytskyj

kolesnytskiy@vntu.edu.ua

Vinnytsia National Technical University

http://orcid.org/0000-0003-0336-4910

Oleksandr Reshetnik

Degratnik@gmail.com

Vinnytsia National Technical University

http://orcid.org/0009-0006-7320-329X

Saule Smailova

Saule_Smailova@mail.ru

D.Serikbayev East Kazakhstan State Technical University

http://orcid.org/0000-0002-8411-3584

Ulzhalgas Zhunissova

ulzhalgaszhunisova@gmail.com

Astana Medical University

http://orcid.org/0000-0001-5255-9314

Abstract

The article aims to study the multi-level segmentation process of images of arbitrary configuration and placement based on features of spatial connectivity. Existing image processing algorithms are analyzed, and their advantages and disadvantages are determined. A method of organizing the process of segmentation of multi-gradation halftone images is developed and an algorithm of actions according to the described method is given.

Keywords:

image segmentation, image processing, halftone images, spatial connectivity

References

Avrunin O. G. et al.: Features of image segmentation of the upper respiratory tract for planning of rhinosurgical surgery. 2019 IEEE 39th International Conference on Electronics and Nanotechnology, ELNANO 2019, 485–488. DOI: https://doi.org/10.1109/ELNANO.2019.8783739

Avrunin O. G. et al.: Research Active Posterior Rhinomanometry Tomography Method for Nasal Breathing Determining Violations. Sensors 21, 2021, 8508 [http://doi.org/10.3390/s21248508]. DOI: https://doi.org/10.3390/s21248508

Bradski G., Kaehler A.: Learning Open CV, second edition. 2013.

Burgener F. et al.: Differential Diagnosis in Computed Tomography, 2011. DOI: https://doi.org/10.1055/b-002-76304

Campbell J.: Human Medical Thermography, 2022. DOI: https://doi.org/10.1201/9781003281764

Comaniciu D., Meer P.: Mean shift analysis and applications. IEEE International Conference on Computer Vision 2, 1999, 1197. DOI: https://doi.org/10.1109/ICCV.1999.790416

Comaniciu D., Meer P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 603–619. DOI: https://doi.org/10.1109/34.1000236

Comaniciu D., Ramesh V., Meer P.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. Conference on CVPR 2, 2000, 1–8.

Gonzalez R., Woods R: Digital Image Processing. Technosphere, 2012.

Haralik R. M.: Statistical and structural approaches to the description of textures. Proceedings of the Institute of Electronics and Radio Engineering, 1979, 98–120.

Kurmi Y., Chaurasia V.: Multifeature-based medical image segmentation. Sensors, 2018. DOI: https://doi.org/10.1049/iet-ipr.2017.1020

Linda G. S. Stockman G. C.: Computer Vision, 2001.

Orazayeva A. et al.: Biomedical image segmentation method based on contour preparation. Proc. SPIE 12476, 2022, 1247605 [http://doi.org/10.1117/12.2657929]. DOI: https://doi.org/10.1117/12.2657929

Rodriguez-Lozano F. J., León-García F., Ruiz de Adana M., Palomares J. M., Olivares J.: Non-Invasive Forehead Segmentation in Thermographic Imaging. Sensors 19, 2019, 4096 [http://doi.org/10.3390/s19194096]. DOI: https://doi.org/10.3390/s19194096

Romanyuk O. N.: A function-based approach to real-time visualization using graphics processing units. Proc. SPIE 11581, 2020, 115810E [http://doi.org/10.1117/12.2580212].

Rother С., Kolmogorov V., Blake Grabcut A.: Interactive foreground extraction using iterated graph cuts, 2004. DOI: https://doi.org/10.1145/1186562.1015720

Timchenko L. I. et al.: Q-processors for real-time image processing. Proc. SPIE 11581, 2020, 115810F [http://doi.org/10.1117/12.2580230].

Timchenko L. I., Kutaev Y. F.: Method and organization of image extraction. Patent 2024939С1 RF, MKI G 06 K 9/00, 1992-07-08, 1992.

Vapnik V.N., Chervonenkis A.Y.: Pattern recognition theory (statistical learning problems). Science, 1974.

Wójcik W., Smolarz A.: Information Technology in Medical Diagnostics (1st ed.). CRC Press 2017 [http://doi.org/10.1201/9781315098050]. DOI: https://doi.org/10.1201/9781315098050

Timchenko, L., Kokriatskaya, N., Tverdomed, V., Stetsenko, O., Kaplun, V., Kolesnytskyj, O. K., … Zhunissova, U. (2023). SEGMENTATION OF MULTIGRADATION IMAGES BASED ON SPATIAL CONNECTIVITY FEATURES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(3), 47–50. https://doi.org/10.35784/iapgos.5352

Article Sidebar

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License