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Automated recursive separable algorithm for medical image processing
A.A. Filimontseva 1, A.V. Kamenskiy 1, A.S. Zahlebin 1

FSBEO HE «Tomsk State University of Control Systems and Radioelectronics»,
634050, Russia, Tomsk, Lenin avenue, building 40

 PDF, 1974 kB

DOI: 10.18287/2412-6179-CO-1597

Pages: 1023-1029.

Full text of article: English language.

Abstract:
In the modern world, there is an increasing demand for automated information processing systems that reduce the time spent on monotonous human work, increase the efficiency of algorithms and the time it takes to solve assigned tasks. The paper presents the development of an automated recursive-separable digital filter for processing digital medical images. The feature of the automated algorithm is its speed; due to its internal structure, the recursive-separable Laplacian Filter “double pyramid” performs separate processing by row and column of the image, using recirculators. The recirculator itself also affects the speed due to the use of recursion properties. Automation of the algorithm will increase the efficiency of digital image processing, due to automatic enumeration of filter coefficients, which will improve the quality of the original information almost without human intervention. The developed algorithm itself carries out the process of image quality assessment and repeats the digital processing procedure until it reaches the required values of one of the parameters: mean square error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity index of image (SSIM). To test the developed automated algorithm, the software "ALF: Automated LDP Filter" was developed and implemented using QtDesigner and PySide6. The software module consists of three data entry areas, such as the mask size h, the coefficient for lifting A1 and the coefficient for increasing the central element A2, two areas for displaying the image, the original and processed, seven buttons, such as input, output and saving the image, checking the image quality condition, three buttons for calculating the parameters MSE, PSNR and SSIM. As part of the testing, a study was carried out on the performance of the automated recursive separable digital filter using the example of endoscopic images obtained from a robotic surgical complex, which showed the effectiveness of its use, since it was possible to achieve the specified parameter values on the output image.

Keywords:
automated algorithm, recursive separable filter, peak signal-to-noise ratio, root mean square error, structure similarity.

Citation:
Filimontseva AA, Kamenskiy AV, Zahlebin AS. Automated recursive separable algorithm for medical image processing. Computer Optics 2025; 49(6): 1023-1029. DOI: 10.18287/2412-6179-CO-1597.

References:

  1. Grechishkina EI, Yaduta AZ. Analysis of digital processing of medical images [In Russian]. Collection of selected articles based on the materials of scientific conferences of the State Research Institute "National Development". Saint-Petersburg; 2021: 32-35.
  2. Sagheer SVM, George SN. A review on medical image denoising algorithms. Biomed Signal Process Control 2020; 61: 102036. DOI: 10.1016/J.BSPC.2020.102036.
  3. Pugin EV, Zhiznyakov AL, Titov DV. Segmentation of images of blood vessels of the fundus using fuzzy image representation [In Russian]. News of the South-West State University 2018; 1(76): 6-17. DOI: 10.21869/2223-1560-2018-22-1-6-17.
  4. Shagalova PA, Sokolova ES, Ryndov SN. Automation of blood microscopy image processing [In Russian]. Proc Int Conf on Computer Graphics and Vision “Graphicon” 2022; 32: 1157-1164.
  5. Lefebvre AEYT, Ma D, Kessenbrock K, Lawson DA, Digman MA. Automated segmentation and tracking of mitochondria in live-cell time-lapse images. Nat Methods 2021; 18(9): 1091-1102. DOI: 10.1038/s41592-021-01234-z.
  6. Heydon P, Egan C, Bolter L, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol 2021; 105(5): 723-728. DOI: 10.1136/bjophthalmol-2020-316594.
  7. Czerniawski T, Leite F. Automated digital modeling of existing buildings: A review of visual object recognition methods. Autom Constr 2020; 113: 103131. DOI: 10.1016/j.autcon.2020.103131.
  8. Su J, Li Z, Shao X, Ji C, Ji R, Zhou R, et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020; 91(2): 415-424.e4. DOI: 10.1016/j.gie.2019.08.026.
  9. Sheshkus A, Chirvonaya A, Arlazarov VL. Tiny CNN for feature point description for document analysis: approach and dataset. Computer Optics 2022; 46(3): 429-435. DOI: 10.18287/2412-6179-CO-1016.
  10. Zebari DA, Zeebaree DQ, Abdulazeez AM, Haron H, Hamed HNA. Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access 2020; 8: 203097-203116. DOI: 10.1109/ACCESS.2020.3036072.
  11. Wang S, Li C, Wang R, et al. Annotation-efficient deep learning for automatic medical image segmentation. Nat Commun 2021; 12: 5915. DOI: 10.1038/s41467-021-26216-9.
  12. Öztürk Ş, Ahmad R, Akhtar N. Variants of Artificial Bee Colony algorithm and its applications in medical image processing. Appl Soft Comput 2020; 97: 106799. DOI: 10.1016/j.asoc.2020.106799.
  13. Xue Z, Angara S, Levitz D, Antani S. Analysis of digital noise and reduction methods on classifiers used in automated visual evaluation in cervical cancer screening. Proc SPIE 2022; 11950: 1195008. DOI: 10.1117/12.2610235.
  14. Kamenskiy AV. High-speed recursive-separable image processing filters. Computer Optics 2022; 46(4): 659-665. DOI: 10.18287/2412-6179-CO-1063.
  15. Rylov KA, Kupriyanova KS, Kamensky AV. Influence of unequilateral apertures of digital filters laplacian “Trunced pyramid” and “Double pyramid” on the accuracy of television measuring systems. Program Comput Soft 2024; 50: 238-248. DOI: 10.1134/S036176882470004X.
  16. Nechayev AA. Using power mean for image quality assessment. Int J Open Inf Technol 2024; 12(6): 65-75.
  17. Nikin VV, Garina SV. Overview of methods and tools for evaluating the quality of frames in video file. International Journal of Professional Science 2020; 11: 56-63.
  18. Kartashevsky VG, Mamyshev NR. Video quality assessment. MPQM, SSIM, NQM metrics [In Russian]. Development of modern technologies: Theoretical and practical aspects 2022; 30-34.
  19. Sai SV. A method for assessing photorealistic image quality with high resolution. Computer Optics 2022; 46(1): 121-129. DOI: 10.18287/2412-6179-CO-899.
  20. Sai SV, Kamensky AV, Kuryachy MI. Modern methods of analyzing and improving the digital images quality [In Russian]. Khabarovsk: Publishing house of the Pacific State University; 2020.
  21. Filimontseva AA, Kamenskiy AV. ALF: Automated LDP Filter [In Russian]. Certificate of state registration of the computer program No. 2024665224 of June 27, 2024.

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