Abstract
General methods of image processing, analysis and enhancement and their biomedical applications developed by the scientific school of the Laboratory of Mathematical Methods of Image Processing of the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University are reviewed. The suggested general methods and algorithms of image quality enhancement for image resampling and super-resolution, ringing artifact reduction, image sharpening, image denoising, and image registration are described. Image analysis methods based on Hermite projection method, Gauss-Laguerre functions and the use of phase information are presented. We describe and review the developed methods for medical imaging tasks solution, including problems in histology, color Doppler flow mapping, ultrasound liver fibrosis diagnostics, CT brain perfusion, Alzheimer’s disease diagnostics, dermatology, chest X-ray image analysis, live cell image registration, tracking, segmentation and synthesis. The paper illustrates the basic research idea of the effectiveness of the hybrid approach when we jointly use classical mathematical methods and deep learning approaches.
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Ya. A. Pchelintsev, A. V. Khvostikov, A. S. Krylov, L. E. Parolina, N. A. Nikoforova, L. P. Shepeleva, E. S. Prokop’ev, M. Farias, and D. Yong, “Hardness analysis of X-ray images for neural-network tuberculosis diagnosis,” Comput. Math. Model. 33, 230–243 (2022). https://doi.org/10.1007/s10598-023-09568-3
Ya. Pchelintsev, A. Nasonov, A. Krylov, S. Enoki, and Ya. Okada, “Enhancement algorithms for blinking fluorescence imaging,” in Proc. 2019 4th Int. Conf. on Biomedical Imaging, Signal Processing, Nagoya, Japan, 2019 (Association for Computing Machinery, New York, 2019), pp. 72–77. https://doi.org/10.1145/3366174.3366183
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M. A. Penkin, A. S. Krylov, and A. V. Khvostikov, “Hybrid method for Gibbs-ringing artifact suppression in magnetic resonance images,” Program. Comput. Software 47, 207–214 (2021). https://doi.org/10.1134/s0361768821030087
I. Peterlík, D. Svoboda, V. Ulman, D. Sorokin, and M. Maška, “Model-based generation of synthetic 3D time-lapse sequences of multiple mutually interacting motile cells with filopodia,” in Simulation and Synthesis in Medical Imaging. SASHIMI 2018, Ed. by A. Gooya, O. Goksel, I. Oguz, and N. Burgos, Lecture Notes in Computer Science, 2018, Vol. 11037, pp. 71–79. https://doi.org/10.1007/978-3-030-005368_8
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V. A. Pyatov and D. V. Sorokin, “Affine registration of histological images using transformer-based feature matching,” Pattern Recognit. Image Anal. 32, 626–630 (2022). https://doi.org/10.1134/s1054661822030324
E. Safronova and E. Pavelyeva, “Unsupervised palm vein image segmentation,” CEUR Workshop Proc. 2744, 40 (2020). https://doi.org/10.51130/graphicon-2020-2-3-40
T. B. Sagindykov, A. R. Brazhe, and D. V. Sorokin, “Preprocessing and registration of miniscope-based calcium imaging of the rodent brain,” ISPRS J. Photogrammetry Remote Sensing, No. 42-2/W12, 185–188 (2019). https://doi.org/10.5194/isprs-archives-XLII-2-W12-1852019
T. B. Sagindykov and E. A. Pavelyeva, “Human image matting based on convolutional neural network and principal curvatures,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 44, 183–187 (2021). https://doi.org/10.5194/isprsarchives-XLIV-2-W1-2021-183-2021
A. Semashko, A. Yatchenko, A. Krylov, A. Bezugly, N. Makhneva, and N. Potekaev, “Border extraction of epidermises, derma and subcutaneous fat in high-frequency ultrasonography,” in Proc. 22nd Int. Conf. on Computer Graphics and Vision GraphiCon’2012 (Moscow, 2012), pp. 73–75.
I. Sitdikov, F. Guryanov, and A. S. Krylov, “Accelerated mutual entropy maximization for biomedical image registration,” in 2015 Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Orleans, France, 2015 (IEEE, 2015), Vol. 337, p. 340. https://doi.org/10.1109/ipta.2015.7367160
I. T. Sitdikov and A. S. Krylov, “Variational image deringing using varying regularization parameter,” Pattern Recognit. Image Anal. 25, 96–100 (2015). https://doi.org/10.1134/s1054661815010186
D. V. Sorokin, E. A. Arifulin, Ye. S. Vassetzky, and E. V. Sheval, “Live-cell imaging and analysis of nuclear body mobility,” in The Nucleus, Ed. by R. Hancock, Methods in Molecular Biology, Vol. 2175 (Springer, New York, 2020), pp. 1–9. https://doi.org/10.1007/978-1-0716-0763-3_1
D. V. Sorokin and A. S. Krylov, “Short reference image quality estimation using modified angular edge coherence,” in Proc. 20th Int. Conf. on Computer Graphics and Vision GraphiCon’2010 (St. Petersburg, 2010), Vol. 137, p. 140.
D. V. Sorokin and A. S. Krylov, “A projection local image descriptor,” Pattern Recognit. Image Anal. 22, 380–385 (2012). https://doi.org/10.1134/s1054661812020162
D. V. Sorokin, M. M. Mizotin, and A. S. Krylov, “Gauss–Laguerre keypoints extraction using fast hermite projection method,” in Image Analysis and Recognition. ICIAR 2011, Ed. by M. Kamel and A. Campilho, Lecture Notes in Computer Science, Vol. 6753 (Springer Berlin Heidelberg, 2011), pp. 284–293. https://doi.org/10.1007/978-3-642-21593-3_29
D. V. Sorokin, I. Peterlik, M. Tektonidis, K. Rohr, and P. Matula, “Non-rigid contour-based registration of cell nuclei in 2D live cell microscopy images using a dynamic elasticity model,” IEEE Trans. Med. Imaging 37, 173–184 (2018). https://doi.org/10.1109/tmi.2017.2734169
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D. V. Sorokin, I. Peterlik, V. Ulman, D. Svoboda, T. Necasova, K. Morgaenko, L. Eiselleova, L. Tesarova, and M. Maska, “FiloGen: A model-based generator of synthetic 3D time-lapse sequences of single motile cells with growing and branching filopodia,” IEEE Trans. Med. Imaging 37, 2630–2641 (2018). https://doi.org/10.1109/tmi.2018.2845884
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D. I. Sungatullina, A. S. Krylov, and D. N. Fedorov, “Fast registration algorithms for histological images,” Nauchnaya Vizualizatsiya 6 (4), 61–71 (2014).
A. S. Thomaz Aline, A. S. Lima Jonathan, C. J. Miosso, C. Q. Farias Mylene, A. S. Krylov, and Y. Ding, “Undersampled magnetic resonance image reconstructions based on a combination of U-Nets and L1, L2, and TV optimizations,” in 2022 IEEE Int. Conf. on Imaging Systems and Techniques (IST), Kaohsiung, Taiwan, 2022 (IEEE, 2022), pp. 1–6. https://doi.org/10.1109/ist55454.2022.9827727
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ACKNOWLEDGMENTS
The composition of the scientific school includes members of the Laboratory of Mathematical Methods of Image Processing, its deceased co-founder Dmitrii V. Yurin, former members Artem M. Yatchenko, Maksim M. Mizotin, and Aleksei S. Lukin, current PhD students Maksim Penkin, Dmitrii Lukov, Valerii E. Karnaukhov, Nadezhda A. Anoshina, Tamerlan B. Sagindykov, students who defended their PhD Mohsen Najafi, Anton A. Gorokhov, Danil N. Korchagin, Vladmir N. Tsibanov, Aleksandra A. Nasonova (Chernomorets), Aleksei V. Umnov, Andrei A. Dovganich and more than 150 current and former master students. We are grateful to them and to all our collaborators for many years of joint work.
We sincerely thank Professor Aleksandr M. Denisov, Professor Aleksandr V. Razgulin, Professor Evgenii V. Sheval, Professor Pavel G. Malkov, and Il’ya A. Mikhailov (Moscow State University); Professor Srdjan Stanković and Professor Irena Orović (University of Montenegro); Academician Valerii A. Sandrikov, the head of laboratory Tat’yana Yu. Kulagina and Professor Julius R. Kamalov (Petrovsky National Research Center of Surgery); Professor Vasilii A. Lukshin and Academician Dmitrii Yu. Usachev (Burdenko National Medical Research Center of Neurosurgery); Professor Jenny Benois-Pineau (University of Bordeaux); Professor Natal’ya V. Makhneva (Vladimirsky Moscow Regional Research and Clinical Institute); Professor Liubov’ E. Parolina (National Medical Research Center for Phthisiopulmonology and Infectious Diseases); Еgor S. Prokopеv (Andreev Scientific Practical Phthisiology Center); Dr. Pavel Matula and Dr. Martin Maška (Masaryk University); Professor Yasushi Okada (University of Tokyo) and other scientific colleagues for the productive collaboration.
We are also really thankful to our current and former partners in long time joint international projects for the possibility to participate in their activity: Professor Kuo-Liang Chung (National Taiwan University of Science and Technology) [9], Professor Yong Ding (Zhejiang University) [10, 20, 118], and Professor Mylène C.Q. Farias (University of Brasília) [120].
Funding
The results and the created ideas and methods are used and further developed in the histological image analysis project supported by the Russian Science Foundation grant no. 22‑41‑02002.
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Andrey S. Krylov graduated from Lomonosov Moscow State University in 1978, received PhD degree and Doctor degree in applied mathematics in Lomonosov Moscow State University in 1983 and 2009, respectively. Since 1978 he has been working in the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University. Now, he is a Full Professor, Head of the Laboratory of Mathematical Methods of Image Processing. Editor-in-Chief of Springer journal Computational Mathematics and Modeling since 2020. In 2016–2021 Member of Conference Programme Committies of more than 45 International Conferences. He was awarded the Leninskii Komsomol Prize in Science and Engineering in 1989. His research interests include mathematical methods of image processing and computer vision.
Andrey V. Nasonov graduated from Lomonosov Moscow State University in 2007 and received PhD degree in applied mathematics in Lomonosov Moscow State University in 2011. Now he is a senior researcher at Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University. His research interests include mathematical methods of image processing and analysis, regularization methods.
Dmitry V. Sorokin graduated from Lomonosov Moscow State University and received PhD degree in applied mathematics from Lomonosov Moscow State University, in 2008 and 2011. From 2012 till 2017 he was a postdoc researcher at the Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic. Since 2018 he works at the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University as a senior researcher at the Laboratory of Mathematical Methods of Image Processing. In 2018 he was awarded the Moscow government prize for young scientists. He is a member of program committees of several annual international conferences and serves as a reviewer for IEEE Transaction of Medical Imaging. His research interests include mathematical methods of image processing and analysis, image registration, biomedical image analysis, deep learning.
Alexander V. Khvostikov graduated from Lomonosov Moscow State University in 2015 and received PhD degree in applied mathematics from Lomonosov Moscow State University in 2019. Since then, he works in the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University as a researcher in the Laboratory of Mathematical Methods of Image Processing. In 2020 he was awarded the prize of the Lomonosov Moscow State University for young teachers and researchers who have achieved significant results in teaching and research activities. His research interests include image processing and analysis of medical images, computer vision, machine learning, deep learning, convolutional neural networks, hybrid methods.
Elena A. Pavelyeva graduated from Lomonosov Moscow State University in 2008 and received PhD degree in applied mathematics from Lomonosov Moscow State University in 2015. Currently, she works as a Senior Lecturer at the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University. Her research interests include image processing and analysis, biometrics, computer vision.
Yakov A. Pchelintsev received the B.S. degree and M.S. degree in applied mathematics and informatics from Lomonosov Moscow State University in 2017 and 2019. Now, he is a PhD student in the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University in the Laboratory of Mathematical Methods of Image Processing. His research interests include image processing and analysis, machine learning, deep learning, convolutional neural networks, hybrid methods.
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Krylov, A.S., Nasonov, A.V., Sorokin, D.V. et al. Image Analysis and Enhancement: General Methods and Biomedical Applications. Pattern Recognit. Image Anal. 33, 1493–1514 (2023). https://doi.org/10.1134/S1054661823040235
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DOI: https://doi.org/10.1134/S1054661823040235