(49-4) 09 * << * >> * Russian * English * Content * All Issues
Methods of digital processing of medical optical images to improve image quality and disease diagnostics accuracy: review
T.M. Akaeva 1, A.V. Kamenskiy 1, N. Borodina 1, V.N. Pavlov 2, A.R. Bilyalov 2, A.M. Avzaletdinov 2, T.D. Vildanov 2
1 Tomsk State University of Control Systems and Radioelectronics,
40 Prospekt Lenina, Tomsk, 634050, Russia;
2 Bashkir State Medical University, Ministry of Health of the Russian Federation,
3 Lenina St., Ufa, 450008, Russia
PDF, 7337 kB
DOI: 10.18287/2412-6179-CO-1554
Pages: 593-623.
Full text of article: Russian language.
Abstract:
An overview of the main methods of optical medical imaging as well as sources and types of noise is presented. Various problems in the formation of optical medical images are analyzed. A detailed analysis of modern methods of processing of digital medical optical images is carried out.
Keywords:
optics, optical images, digital images, medical image processing, filtering.
Citation:
Akaeva TM, Kamenskiy AV, Borodina N, Pavlov VN, Bilyalov AR, Avzaletdinov AM, Vildanov TD. Methods of digital processing of medical optical images to improve image quality and disease diagnostics accuracy: review. Computer Optics 2025; 49(4): 593-623. DOI: 10.18287/2412-6179-CO-1554.
References:
- Fedotov AA. Fundamentals of digital biomedical image processing [In Russian]. Samara: SSAU Publishing House; 2013. ISBN: 978-5-7883-0951-4.
- Homidov ME, Goipov EA. Methods for processing biomedical signals and images [In Russian]. Universum: Tech Sci 2020; 8(77): 32-34.
- Perminov AV, Fajzrahmanova IS. Applied holography. Lecture course [In Russian]. Perm: Perm National Research Polytechnic University; 2017.
- Bouchard KN, Pukall CF. Validation of laser Doppler flowmetry for the continuous measurement of women's genital response. Psychophysiology 2023; 60(5): e14230. DOI: 10.1111/psyp.14230.
- Filippova KP. Explore a range of features and capabilities of the optical microscope [In Russian]. In Book: START IN SCIENCE 2023: Collection of Articles of the III International Research Competition. Penza: Science and Enlightenment (IP Gulyaev GYu); 2023: 129-133.
- Shahnovich IV. Modern scanning electron microscopes: increasingly complex, increasingly simpler [In Russian]. Laboratory and Production 2018; 2: 78-91.
- Kornilov VM, Galiev AF. Fundamentals of scanning probe microscopy [In Russian]. Ufa: M Akmulla Bashkir State Pedagogical University Publisher; 2011.
- Fridman MV, Kosareva AA, Snezhko EV, Kamlach PV, Kovalyov VA. Methodology of forming a database of histopathologic images of papillary thyroid cancer for deep learning [In Russian]. Informatic 2023; 20(2): 28-38. DOI: 10.37661/1816-0301-2023-20-2-28-38
- Bakhritdinova FA, Urmanova FM, Tuichibaeva DM. Diagnostic role of optical coge-rent tomography angiography in diabetic retinopathy [In Russian]. Advanced Ophthalmology 2023; 2(2): 29-34.
- Davoudi B, Morrison M, Bizheva K, Yang VXD, Dinniwell R, Levin W, Vitkin IA. Optical coherence tomography platform for microvascular imaging and quantification: initial experience in late oral radiation toxicity patients. J Biomed Opt 2013; 18(7): 076008. DOI: 10.1117/1. jbo.18.7.076008.
- Radu MD, Räber L, Garcia-Garcia HM, Serruys PW. The clinical atlas of intravascular optical coherence tomography. Toulouse: Europa Edition; 2012.
- Orlova VA, Fiks II, Kleshnin MS, Plekhanov VI, Shakhova NM, Turchin IV, Kamensky VA. Optical diffusion tomography (ODT) for the diagnosis of breast cancer [In Russian]. Almanac of Clinical Medicine 2008; 17(1): 62-64.
- Martins IS, Silva HF, Lazareva EN, et al. Measurement of tissue optical properties in a wide spectral range: a review. Biomed Opt Express 2023; 14(1): 249-298. DOI: 10.1364/BOE.479320.
- Nioka S, Yung Y, Shnall M, Zhao S, Orel S, Xie C, Solin L. Optical imaging of breast tumor by means of continuous waves. In Book: Nemoto EM, LaManna JC, eds. Oxygen transport to tissue XVIII. New York: Springer Science+Business Media; 1997: 227-232. DOI: 10.1007/978-1-4615-5865-1_27.
- Xu Y, Iftimia N, Jiang H, Key LL, Bolster MB. Imaging of in vitro and in vivo bones and joints with continuous-wave diffuse optical tomography. Opt Express 2001; 8(7): 447-451. DOI: 10.1364/oe.8.000447.
- Bogdanov AK, Protsenko VD. Practical applications of modern methods of image analysis in medicine [In Russian]. Moscow: "RUDN" Publisher; 2008.
- Strelkova AN, Trufanov MI, Stepchenko AA. Mathematical model for the restoration of endoscopic images [In Russian]. Izvestia VUZov: Priborostroenie 2009; 52(2): 70-74.
- Lozovaya VV, et al. Endoscopic differential diagnosis of gastritis-like form of primary non-Hodgkin's lymphoma and gastric neuroendocrine tumors [In Russian]. Pelvic Surgery and Oncology 2023; 13(2): 27-37.
- Kazarinov AV, Okhotnikov SA, Batukhtin DM. Algorithm for pre-processing endoscopic images of the gastrointestinal tract [In Russian]. Optical-Electronic Instruments and Devices in Systems for Pattern Recognition, Image Processing and Symbolic Information (Recognition-2018) 2018: 131-133.
- Korolyuk IP. Medical informatics: Textbook [In Russian]. 2nd ed. Samara: Ofort LLC – Samara State Medical University; 2012.
- Narziev JS, Abdullaev SK, Uktamov ShSh. Scintigraphy [In Russian]. Ta'lim va Rivojlanish Tahlili Onlayn Ilmiy Jurnali 2022; 2(3): 1-3.
- Kozyreva TI, Smirnov VВ. Application of rentgenov radiation in medicine [In Russian]. Collection of Scientific Articles on the VIII International Scientific and Practical 2022: 65-73.
- Denisova NV, Terekhov IN. Mathematical modeling of the procedure for examining patients using the SPECT method in cardiology: calculation of planar images [In Russian]. Medical Physics 2015; 3: 32-39.
- Gulamov ShA. Modern methods of visualization in medicine and their study at biophysics lessons [In Russian]. Economy and Society 2023; 11(114): 1080-1083.
- Okbaev MB. Progress in the field of neuroscience: discoveries and prospects of application in clinical practice [In Russian]. Best Intellectual Research 2024; 18(2): 47-57.
- Kreisl WC, Kim MJ, Coughlin JM, Henter ID, Owen DR, Innis RB. PET imaging of neuroinflammation in neurological disorders. The Lancet Neurology 2020; 19(11): 940-950. DOI: 10.1016/S1474-4422(20)30346-X.
- Savelyeva TV, Kashchenko VA. Possibilities of multilayer spiral computed tomography in identifying tumor lesions of the extrahepatic bile ducts [In Russian]. Radiation Diagnostics and Therapy 2010; 3: 54-60.
- Lishov DE, Boyko LV, Zolotukhin IA, et al. Duplex ultrasound of lower limbs venous system. Russian phlebology association expert panel report [In Russian]. Phlebology 2021; 15(4): 318-340. DOI: 10.17116/flebo202115041318.
- Kropotkin EB, Ivanitsky EA, Shlyakov DA, Ivanitskaya YuV, Sakovich VA. Non-fluoroscopic cryoballoon ablation of paroxysmal atrial fibrillation [In Russian]. Bulletin of Arrhythmology 2020; 7(4): 12-16. DOI: 10.35336/VA-2020-4-12-16.
- Pesotsky RS, Kalinin PS, Krivorotko PV, Mishchenko AV, Zernov KYu, Kozyreva KS, Semiglazov VF. Computed tomography in planning diep-flap breast reconstruction [In Russian]. Issues in Oncology 2019; 65(4): 603-607.
- Guzhov VI, Vinokurov AA. Methods for studying the structure and functional state of the brain [In Russian]. Automation and Software Engineering 2014; 3(9): 80-88.
- Zherko OM. Physical bases of ultrasonic diagnostics [In Russian]. Minsk: "BelMAPO" Publisher; 2023.
- Chandra A. Non-invasive imaging of the venous system. In Book: Jimenez JC, Wilson SE, eds. Contemporary management of acute and chronic venous disease. Ch 1. Boca Raton: CRC Press; 2024: 3-10.
- Kukhtenkova VP. Pulse-wave tissue stress Doppler echocardiography with dipyridamole and dobutamine in the diagnosis of coronary heart disease [In Russian]. Radiology-Practice 2012; 1: 23-29.
- Ryding A. Essential echocardiography. Elsevier Health Sciences; 2008.
- Ramesh G. The management and reduction of digital noise in video image processing by using transmission based noise elimination scheme. ICTACT Journal on Image & Video Processing 2022; 13(1): 2797-2801. DOI: 10.21917/ijivp.2022.0398.
- Babynin KI. Study of the effectiveness of the VM3D method in the task of cleaning images from noise [In Russian]. Graduation qualification work. Belgorod: 2019.
- Novikov AI, Pronkin AV. Methods for image noise level estimation. Computer Optics 2021; 45(5): 713-720. DOI: 10.18287/2412-6179-CO-894.
- Roshchin DA. Suppression of additive periodic noise on the image of a sighting target by a Gaussian narrow-bandpass filter [In Russian]. Technical Sciences: Problems and Solutions 2022: 52-57.
- Stolyarov AА. Types of spacial-time tv image processing [In Russian]. Radio-location, Navigation, Communication 2020: 109-112.
- Goodman JW. Some fundamental properties of speckle. J Opt Soc Am 1976; 66(11): 1145-1150. DOI: 10.1364/JOSA.66.001145.
- Timchenko PE. Coherent optics [In Russian]. Electronic textbook. Samara: 2013.
- Gupta S, Chauhan R, Sexana S. Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med Biol Eng Comput 2004; 42(2): 189-192. DOI: 10.1007/BF02344630.
- Achim A, Bezerianos A, Tsakalides P. Novel bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Transact Med Imaging 2001; 20(8): 772-783. DOI: 10.1109/42.938245.
- Achim A, Tsakalides P, Bezerianos A. SAR image denoising via Bayesian wavelet shrinkage based on heavy tailed modeling. IEEE Trans Geosci Remote Sens 2003; 41(8): 1773-1784. DOI: 10.1109/TGRS.2003.813488.
- Pižurica A, Jovanov L, Huysmans B, Zlokolica V, De Keyser P, Dhaenens F, Philips W. Multiresolution denoising for optical coherence tomography: A review and evaluation. Current Medical Imaging Reviews 2008; 4(4): 270-284. DOI: 10.2174/157340508786404044.
- Bondina NN, Kalmychkov AS, Kozina OA. Comparison of filtering algorithms for medical images based on their quality assessments [In Russian]. Bulletin of the National Technical University, Kharkov Polytechnic Institute. Series: Computer Science and Modeling 2013; 39(1012): 15-21.
- Lukashevich MM. Digital image processing and pattern recognition: manual [In Russian]. Minsk: "BGUIR" Publisher; 2023.
- Gusev VYu, Krapivenko AV. Method of filtering periodic noise of digital images [In Russian]. Proceedings of the Moscow Aviation Institute 2012; 50: 34.
- Kandidov VP, Chesnokov SS, Shlenov SA. Discrete Fourier transform. Moscow: Faculty of Physics of Moscow State University Publisher; 2019.
- Tarasov PV, Ushenina IV. Filter-cmi methods used in preparing aerial photos for decording [In Russian]. International Student Scientific Bulletin 2016; 3-1: 93-94.
- Chandra KPB, Gu DW. Nonlinear filtering. Cham, Switzerland: Springer; 2019. ISBN: 978-3-030-01797-2.
- Seitkazin NК. Wavelet analysis for noise filtering in digital signals: a review of methods and applications. In: Modern conditions of integration processes in science and education. Collection of articles based on the results of the International Scientific and Practical Conference (Kazan, April 21, 2023). Sterlitamak: Agency for International Studies; 2023: 54-59.
- Seitkazin NK. Efficiency of wavelet analysis in noise filtering of digital signals. Cooperation of Science and Society as a Tool 2023: 133-137.
- Fisenko VT, Fisenko TYu. Computer processing and image recognition: textbook. Allowance [In Russian]. Saint-Petersburg: "St. Petersburg State University ITMO" Publisher; 2008.
- Belyaev A, Peck KK, Brennan N, Kholodny A. Application of functional magnetic resonance imaging in the clinic. Russian Electronic Journal of Radiation Diagnostics 2014; 4(1): 14-24.
- Filist SA, Tomakova RA, Degtyarev SV, Rybochkin AF. Hybrid intelligent models for image segmentation of chest radiographs [In Russian]. Medical Technology 2017; (5): 41-45.
- Pijanka JK, Markov PP, Midgett D, Paterson NG, White N, Blain EJ, Boote C. Quantification of collagen fiber structure using second harmonic generation imaging and two‐dimensional discrete Fourier transform analysis: application to the human optic nerve head. J Biophotonics 2019; 12(5): e201800376. DOI: 10.1002/jbio.201800376.
- Kala S, Nalesh S, Jose BR, Mathew J. Image reconstruction using novel two-dimensional fourier transform. In Book: Hassanien AE, Oliva DA, eds. Advances in soft computing and machine learning in image processing. Cham: Springer International Publishing; 2017: 699-718. DOI: 10.1007/978-3-319-63754-9_31.
- Ulahovich D. Introduction to digital signal processing [In Russian]. "Litres" Publisher; 2023.
- McNamara R, McCormack J, Jouppi NP. Prefiltered antialased lines using half-plane distance functions. Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Workshop on Graphics Hardware (HWWS '00) 2000: 77-85. DOI: 10.1145/346876.348226.
- Rylov KA, Shipunova KV, Rudnikovich AS. Laboratory practicum "digital image processing" using the divilab software complex [In Russian]. Modern Education: Increasing the Professional Competence of University Teachers – Guaranteeing the Quality of Education 2018: 64-65.
- Zhang Q, Xu L, Jia J. 100+ times faster weighted median filter (WMF). Proc 2014 IEEE Conf on Computer Vision and Pattern Recognition (CVPR '14) 2014: 2830-2837. DOI: 10.1109/CVPR.2014.362.
- Khuzin AA, Methods of image smoothing in noise suppression tasks [In Russian]. Social and economic management: theory and practice 2017; 1: 60-62.
- Gladysheva YuA, Zhilina IV. Development of an algorithm for selecting the size of a smoothing filter mask for x-ray image processing [In Russian]. Biotechnical, Medical and Environmental Systems, Measuring Devices and Robotic Complexes (Biomedsystems-2020) 2020: 240-243.
- Surin VA, Tyrsin AN. Application of the generalized method of smallest modules in problems of image processing and analysis [In Russian]. Bulletin of the Astrakhan State Technical University. Series: Management, computing and information science 2020; 2: 45-55.
- Yang C-C. Finest image sharpening by use of the modified mask filter dealing with highest spatial frequencies. Optik 2014; 125(8): 1942-1944. DOI: 10.1016/j.ijleo.2013.09.070.
- Orhei C, Vasiu R. An analysis of extended and dilated filters in sharpening algorithms. IEEE Access 2023; 11: 81449-81465. DOI: 10.1109/ACCESS.2023.3301453.
- Botik MF, Zapol DA, Mikitchuk EP. Nonlinear methods of image processing of physical experiment [In Russian]. Applied Problems of Optics, Computer Science, Radiophysics and Condensed Matter Physics: Proceedings of the VII International Scientific and Practical Conference dedicated to the 120th Anniversary of the Birth of Academician Anton Nikiforovich Sevchenko, Minsk, May 18-19, 2023. Minsk: "BGU" Publisher; 2023: 201-203.
- Usanov MS, Kulberg NS, Morozov SP. Experience in using adaptive homomorphic filters for processing computer tomograms [In Russian]. Information Technologies and Computing Systems 2017; 2: 33-42.
- Gryaznov AY, Guk KK, Staroverov NE, Kholopova ED. A method for increasing the sharpness and contrast of details in X-ray images [In Russian]. Physical fundamentals of instrumentation 2019; 8(4): 34-37.
- Zafeiridis P, Papamarkos N, Goumas S, Seimenis I. A new sharpening technique for medical images using wavelets and image fusion. J Eng Sci Technol Rev 2016; 9(3): 187-200. DOI: 10.25103/jestr.093.27.
- Agafonov VYu, Fomenkova MA. Methods for non-standard assessment of image sharpness and detail [In Russian]. Eurasian Union of Scientists 2017; 4-2(37): 51-55.
- Bogatyreva VV, Dmitriev AL. Optical methods of information processing [In Russian]. Saint-Petersburg: SPbGUIT-MO Publisher; 2009.
- Tolstunov VA. Harmonic filter with high degree exponential conversion [In Russian]. Current problems of humanities and natural sciences 2017; 2-1: 36-40.
- Tolstunov VA, Shlyndova YuV. Nonlinear smoothing filter with hyperbolic weighting factors [In Russian]. Bulletin of Kemerovo State University 2013; 2(4(56)): 70-74.
- Shah A, et al. Comparative analysis of median filter and its variants for removal of impulse noise from gray scale images. J King Saud Univ – Comput Inf Sci 2022; 34(3): 505-519. DOI: 10.1016/j.jksuci.2020.03.007.
- Satpathy SK, et al. Adaptive non-linear filtering technique for image restoration. arXiv preprint. 2022. Source: <https://arxiv.org/abs/2204.09302>. DOI: 10.48550/arXiv.2204.09302.
- Hwang H, Haddad RA. Adaptive median filters: new algorithms and results. IEEE Trans Image Process 1995; 4(4): 499-502. DOI: 10.1109/83.370679.
- Peixuan Z, Fang L. A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Process Lett 2014; 21(10): 1280-1283. DOI: 10.1109/LSP.2014.2333012.
- Roy A, Singha J, Manam L, Laskar RH. Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Process 2017; 11(6): 352-361. DOI: 10.1049/iet-ipr.2016.0320.
- Myakinin OO. Comparison of algorithms for noise reduction of optical coherence tomography images of skin melanoma [In Russian]. News of higher educational institutions of Russia. Radioelectronics 2020; 23(4): 66-76.
- Stella A, Trivedi B. Implementation of order statistic filters on digital image and OCT image: A comparative study. Int J Mod Eng Res 2012; 2(5): 3143-3145.
- Ongarbaeva AI. Methods of realisation of methods and algorithms of medical image processing with application of operators [In Russian]. Collection of scientific articles of the VIII International Scientific and Practical Conference, Minsk 2022: 112-118.
- Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2(1): 315-337. DOI: 10.1146/annurev.bioeng.2.1.315.
- Nguyen DK, Muravyev SV. Preference aggregation method in determining brightness threshold values for object recognition on optical images [In Russian]. Bulletin of the Tomsk Polytechnic University Geo Assets Engineering 2024; 335(3): 17-30.
- Shubkin EO. Review of medical image segmentation methods [In Russian]. Youth and modern information technologies: collection of proceedings of the XVIII International Scientific and Practical Conference of Students, Graduate Students and Young Scientists, March 22-26, 2021, Tomsk 2021: 90-91.
- Verkhlyutov VM, Gapienko GV. Review of methods for segmentation and triangulation of MRI data [In Russian]. Moscow: Institute of Higher Nervous Activity and Neurophysiology RAS Publisher; 2005: 2-18.
- Hou J, Shi H, Gao W, Lin P, Li B, Shi Y, Matveeva I, Zakharov V, Bratchenko I. The preliminary study of diabetic retinopathy detection based on intensity parameters with optical coherence tomography angiography. Computer Optics 2023; 47(4): 620-626. DOI: 10.18287/2412-6179-CO-1261.
- Gao W, et al. Quantitative assessment of textural features in the early detection of diabetic retinopathy with optical coherence tomography angiography. Photodiagnosis Photodyn Ther 2023; 41: 103214. DOI: 10.1016/j.pdpdt.2022.103214.
- Kochina ML, Kaplin IV, Kovtun NM. Results of using polarized light to examine the eye [In Russian]. Bulletin of problems of biology and medicine 2014; 1(4): 139-146.
- del Río AH, Aranguren I, Oliva D, Elaziz MA, Cuevas E. Efficient image segmentation through 2D histograms and an improved owl search algorithm. Int J Mach Learn & Cyber 2021; 12: 131-150. DOI: 10.1007/s13042-020-01161-z.
- Abd Elaziz M, Ewees AA, Oliva D. Hyper-heuristic method for multilevel thresholding image segmentation. Expert Systems with Applications 2020; 146: 113201. DOI: 10.1016/j.eswa.2020.1132.
- Aranguren I, Valdivia A, Morales-Castañeda B, Oliva D, Abd Elaziz M, Perez-Cisneros M. Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm. Biomedical Signal Processing and Control 2021; 64: 102259. DOI: 10.1016/j.bspc.2020.102259.
- El HSA, Skobtsov YuA, Rodzin SI. Hyperheuristic swarm method for medical image segmentation [In Russian]. Informatization and Communications 2021; 2: 22-29.
- Pozigun MV. Segmentation of medical images based on an object model using CUDA [In Russian]. 2020. Source: <http://elib.spbstu.ru/dl/3/2020/vr/rev/vr20-1756-o.pdf>. DOI: 10.18720/SPBPU/3/2020/vr/vr20-1756/
- Salazar-Gonzalez A, Kaba D, Li Y, Liu X. Segmentation of the blood vessels and optic disk in retinal images. IEEE J Biomed Health Inform 2014; 18(6): 1874-1886. DOI: 10.1109/JBHI.2014.2302749.
- Martynenko TV, Labinskaya DE. Modification of the method for assessing the quality of contour segmentation of radiograph images. Sciences of DonNTU. Series: computing technology and automation 2012; 22(200): 103-108.
- Kozar RV, Navrotsky AA. Algorithms for recognizing medical images in tasks of computer automated diagnostics [In Russian]. Medelectronics 2020. Medical electronics and new medical technologies 2020: 212-219.
- Zhuk SV. Review of modern methods of segmentation of raster images [In Russian]. News of the Volgograd State Technical University 2009; 6: 115-118.
- Ilyasova NYu, Demin NS, Shirokanev AS, Kupriyanov AV, Zamytskiy EA. Method for selection macular edema region using optical coherence tomography data. Computer Optics 2020; 44(2): 250-258. DOI: 10.18287/2412-6179-CO-691.
- Kozar RV, Navrotsky AA, Gurinovich AB. Methods of medical image recognition in computer diagnostics tasks. News of the Gomel State University named after F. Skorina. Series: Natural sciences 2020; 3(120): 116-121.
- Kirillova SV, Kurako MA, Akhmed HYU, Simonov KV. Computational technology for medical image processing: edge extraction [In Russian]. Information and mathematical technologies in science and management 2018; 4(12): 79-87.
- Moseva MC, Kharrasov KR. About the existing methods of noise removal on the image [In Russian]. Inzhenerny Vestnik Dona 2023; 8(104): 5.
- Nixon MS, Aguado AS. Feature extraction and image processing. Replika Press Pvt Ltd; 2002. ISBN: 0-7506-5078-8.
- Ritter GX, Wilson JN. Handbook of computer vision algorithms in image algebra, 2nd ed. CRC Press LLC; 2001. ISBN: 978-0-8493-0075-2.
- Anantrasirichai N, Nicholson L, Morgan JE, Erchova I, Mortlock K, North RV, Achim A. Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography. Comput Med Imaging Graph 2014; 38(6): 526-539. DOI: 10.1016/j.compmedimag.2014.06.012.
- Kokoshkin AV. Application of the constrained renormalization method to the processing of medical ultrasound images [In Russian]. Journal of Radio Electronics 2020; 10: 5.
- Durand F, Dorsey J. Fast bilateral filtering for the display of high-dynamic-range images. Proceedings of the 29th annual conf on Computer graphics and interactive techniques (SIGGRAPH '02) 2002: 257-266. DOI: 10.1145/566570.566574.
- Collection of edge-preserving filtering algorithms – non-local mean NLM filters [In Russian]. 2024. Source: <https://russianblogs.com/article/9452134286/>.
- Zhang X. A modified non-local means using bilateral thresholding for image denoising. Multimed Tools Appl 2024; 83(3): 7395-7416. DOI: 10.1007/s11042-023-15928.
- Guo A, Fang L, Qi M, Li S. Unsupervised denoising of optical coherence tomography images with nonlocal-generative adversarial network. IEEE Transactions on Instrumentation and Measurement 2020; 70: 5000712. DOI: 10.1109/TIM.2020.3017036.
- Tang C, Cao L, Chen J, Zheng X. Speckle noise reduction for optical coherence tomography images via non-local weighted group low-rank representation. Laser Phys Lett 2017; 14(5): 056002. DOI: 10.1088/1612-202X/aa5690.
- Cheng J, Tao D, Quan Y, Wong DWK, Cheung GCM, Akiba M, Liu J. Speckle reduction in 3D optical coherence tomography of retina by A-scan reconstruction. IEEE Trans Med Imaging 2016; 35(10): 2270-2279. DOI: 10.1109/TMI.2016.2556080.
- Rudnitsky AG. Using the nonlocal averaging method to separate heart sounds and breathing sounds [In Russian]. Acoustic Magazine 2014; 60(6): 688-688.
- Zhertunova TV. Image noise reduction using an algorithm based on non-local averaging [In Russian]. Current problems of the information society in science, culture, education, economics 2018: 226-231.
- Pal R. Block-matching and 3D filtering-based denoising of acoustic images obtained through point contact excitation and detection method. Appl Acoust 2024; 217: 109843. DOI: 10.1016/j.apacoust.2023.109843.
- Katkovnik V, Danielyan A, Egiazarian K. Decoupled inverse and denoising for image deblurring: variational BM3D-frame technique. 2011 18th IEEE Int Conf on Image Processing 2011: 3453-3456. DOI: 10.1109/ICIP.2011.6116455.
- Xie K, Luo M, Chen H, Yang M, He Y, Liao P, Zhang Y. Speckle denoising of optical coherence tomography image using residual encoder–decoder CycleGAN. Signal, Image and Video Process 2023; 17(4): 1521-1533. DOI: 10.1007/s11760-022-02361-6.
- Lin CH, Liao WM, Liang JW, Chen PH, Ko CE, Yang CH, Lu CK. Denoising performance evaluation of automated age-related macular degeneration detection on optical coherence tomography images. IEEE Sensor J 2020; 21(1): 790-801. DOI: 10.1109/JSEN.2020.3014254.
- Ivanov VA, Kirichuk VS. Specific features of operation of fallen person detection algorithms based on a sequence of scene images. Optoelectron Instrum Data Process 2011; 47(2): 114-123. DOI: 10.3103/S8756699011020026.
- Wang L. A hexagon-based method for polygon generalization using morphological operators. Int J Geogr Inf Sci 2023; 37(1): 88-117. DOI: 10.1080/13658816.2022.2108036.
- Ghosh P. SkinNet-16: A deep learning approach to identify benign and malignant skin lesions. Front Oncol 2022; 12: 931141. DOI: 10.3389/fonc.2022.931141.
- Aggarwal V, Gupta A. Integrating morphological edge detection and mutual information for nonrigid registration of medical images. Curr Med Imaging Rev 2019; 15(3): 292-300. DOI: 10.2174/1573405614666180103163430.
- Huan H. The research on image processing based on wavelet analysis. 2022 IEEE 10th Joint Int Information Technology and Artificial Intelligence Conf (ITAIC) 2022; 10: 1162-1165. DOI: 10.1109/ITAIC54216.2022.9836655.
- Ouahabi A. A review of wavelet denoising in medical imaging. 2013 8th Int Workshop on Systems, Signal Processing and their Applications (WoSSPA) 2013: 19-26. DOI: 10.1109/WoSSPA.2013.6602330.
- Zabeyvorota OYu, Saitgalina AD, Gubaidullin IM. Use of parallel computing to implement a program for construction of ECG signal graph using wavelet analysis [In Russian]. Scientific service on the Internet: search for new solutions 2012: 111-116.
- Grinchenko NN, Tarasov AS. Development of an algorithm for recognizing license plates [In Russian]. Problems of information transmission and processing in telecommunication networks and systems 2015: 216-217.
- Grinchenko NN, Potapova VYu. Algorithm for searching images in a database [In Russian]. Problems of information transmission and processing in telecommunication networks and systems 2015: 161-164.
- Romanchak VM, Gundina MA. Standard and singular wavelet analysis [In Russian]. System analysis and applied informatics 2020; (4): 39-44.
- Zainidinov KhN, Zhuraev ZHU, Yusupov I, Zhabbarov ZhS. Digital processing of medical images in piecewise polynomial Haar bases [In Russian]. Automation and software engineering 2020; 3(33): 16-23.
- Sdiri B, Kaaniche M, Cheikh FA, Beghdadi A, Elle OJ. Efficient enhancement of stereo endoscopic images based on joint wavelet decomposition and binocular combination. IEEE Trans Med Imaging 2018; 38(1): 33-45. DOI: 10.1109/TMI.2018.2853808.
- Gopi VP, Palanisamy P, Niwas SI. Capsule endoscopic colour image denoising using complex wavelet transform. In Book: Venugopal KR, Patnaik LM, eds. Wireless Networks and Computational Intelligence. 6th International Conference on Information Processing, ICIP 2012, Bangalore, India, August 10-12, 2012. Proceedings. Berlin, Heidelberg: Springer-Verlag; 2012: 220-229. DOI: 10.1007/978-3-642-31686-9_26.
- Anodina-Andrievskaya EM, Bozhokin SV, Marusina MYa, Polonsky YuZ, Suvorov NB. Promising approaches to analyzing the information content of physiological signals and medical images of a person during intellectual activity [In Russian]. News of higher educational institutions. Instrumentation 2011; 54(7): 27-34.
- Yang Y, Park DS, Huang S, Rao N. Medical image fusion via an effective wavelet-based approach. EURASIP J Adv Signal Process 2010; 2010: 44. DOI: 10.1155/2010/579341.
- Chen Y-T, Tseng D-C. Wavelet-based medical image compression with adaptive prediction. Comput Med Imaging Graph 2007; 31(1): 1-8. DOI: 10.1016/j.compmedimag.2006.08.003.
- Bulanova Y.A. Studies of methods to increase the contrast of mammographic images [In Russian]. Oriental renaissance: Innovative, educational, natural and social sciences 2022; 2(10): 304-315.
- Mamatov N, et al. Methods of image contrast enhancement in multispiral computed tomography [In Russian]. Eurasian Journal of Academic Research 2023; 3(9): 125-132.
- Omarova G. et al. Combination of contrast-limited adaptive histogram equalization and gamma correction method to enhance medical image. Series of Physical and Mathematical Sciences 2022; 3: 215-227. DOI:https://doi.org/10.51889/5259.2022.92.12.025.
- Cao G. Contrast enhancement of brightness-distorted images by improved adaptive gamma correction. Comput Electr Eng 2017; 66: 531-544. DOI: 10.1016/j.compeleceng.2017.09.012.
- Zhang G, et al. A medical endoscope image enhancement method based on improved weighted guided filtering. Mathematics 2022; 10(9): 1423. DOI: 10.3390/math10091423.
- Arigela S. A locally tuned nonlinear technique for color image enhancement. WSEAS Trans Signal Process 2008; 4(8): 514-519.
- Agafonova RR, Mingalev AV, Shusharin SN. Methods of histogram processing of a thermal image [In Russian]. Engineering Bulletin of the Don 2019; 1(52): 22.
- Gonzalez R, Woods R. Digital image processing. 2nd ed. Prentice Hall; 2002. ISBN: 978-0201180756.
- Noskovich АN, Navrotsky AA. Improved visual quality of laryngeal imaging [In Russian]. Information Technologies and Systems 2016: 308-309.
- Hai Y, Li L, Gu J. Image enhancement based on contrast limited adaptive histogram equalization for 3D images of stereoscopic endoscopy. 2015 IEEE Int Conf on Information and Automation 2015: 668-672. DOI: 10.1109/ICInfA.2015.7279370.
- Patel S, Goswami M. Comparative analysis of histogram equalization techniques. 2014 Int Conf on Contemporary Computing and Informatics (IC3I) 2014: 167-168. DOI: 10.1109/IC3I.2014.7019808.
- Ravshanov N, Suvanov SM, Saitiev OA. Image sharpening using subimage histogram equalization [In Russian]. Problems of computational and applied mathematics 2019; 6: 37-43.
- Panigrahi S, Roul A, Dash R. A pixel dependent adaptive gamma correction based image enhancement technique. In Book: Das AK, Nayak J, Naik B, Vimal S, Pelusi D, eds. Computational intelligence in pattern recognition. Proceedings of CIPR. Singapore: Springer Nature Singapore Pte Ltd; 2022: 141-150. DOI: 10.1007/978-981-19-3089-8_14.
- Huang L. Efficient contrast enhancement with truncated adaptive gamma correction. 9th Int Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) 2016: 189-194. DOI: 10.1109/CISP-BMEI.2016.7852706.
- Tao L, Asari VK. Adaptive and integrated neighborhood-dependent approach for nonlinear enhancement of color images. J Electr Imaging 2005; 14(4): 043006. DOI: 10.1117/1.2136903.
- Mussaa ES, et al. Comparative study of multi-scale retinex with adaptive and integrated neighborhood-dependent enhancement methods for captured images at different camera aperture. Al-Mustansiriyah Journal of Science 2013; 24(5): 329-344.
- Skripichnikova UA. Methods to improve the quality of endoscopic (medical) images [In Russian]. SCORNIK reports of the 10th scientific and technical school-seminar "Info-communication technologies in the digital world" 2020: 62-63.
- Il'in AG, Kazancev GD, Kostevich AG, Kurjachij MI, Pustynsij IN, Shalimov VA. Digital television in video information systems [In Russian]. Tomsk: Publishing House of Tomsk State University of Control Systems; 2010.
- Kamenskiy AV. High-speed recursive-separable image processing filters. Computer Optics 2022; 46(4): 659-665. DOI: 10.18287/2412-6179-CO-1063.
- Cao N, Liu Y. High-noise grayscale image denoising using an improved median filter for the adaptive selection of a threshold. Appl Sci 2024; 14(2): 635. DOI: 10.3390/app14020635.
- Kamenskiy AV, Kuryachiy MI, Krasnoperova AS, Ilyin YV, Akaeva ТМ, Boyarkin SE. High-speed recursive-separable image processing filters with variable scanning aperture sizes. Computer Optics 2023; 47(4): 605-613. DOI: 10.18287/2412-6179-CO-1240.
.
© 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