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Pattern Recognition and Image Analysis

Erschienen in:

01.12.2023 | SCIENTIFIC SCHOOLS OF THE LOMONOSOV MOSCOW STATE UNIVERSITY (MSU), MOSCOW, THE RUSSIAN FEDERATION

Image Analysis and Enhancement: General Methods and Biomedical Applications

verfasst von: A. S. Krylov, A. V. Nasonov, D. V. Sorokin, A. V. Khvostikov, E. A. Pavelyeva, Ya. A. Pchelintsev

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 4/2023

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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.

Vorheriger Artikel Scientific School “Models and Methods for Processing Video Information of Spatially Distributed Data, Pattern Recognition, Geoinformation Technologies”

Nächster Artikel Data Analysis and Interpretation: Methods of Computer-Aided Measuring Transducer Theory, Morphological Analysis, Possibility Theory, and Subjective Mathematical Modeling

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K. Aderghal, A. Khvostikov, A. Krylov, J. Benois-Pineau, K. Afdel, and G. Catheline, “Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning,” in 2018 IEEE 31st Int. Symp. on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018 (IEEE, 2018), pp. 345–350. https://doi.org/https://doi.org/10.1109/cbms.2018.00067

N. A. Anoshina, A. S. Krylov, and D. V. Sorokin, “Correlation-based 2D registration method for single particle cryo-EM images,” in 2017 Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Montreal, 2017 (IEEE, 2017), pp. 1–6. https://doi.org/10.1109/ipta.2017.8310125

N. A. Anoshina, T. B. Sagindykov, and D. V. Sorokin, “A method for generation of synthetic 2D and 3D cryo-EM images,” Program. Comput. Software 44, 240–247 (2018). https://doi.org/10.1134/s0361768818040023CrossRef

N. A. Anoshina and D. V. Sorokin, “Weak supervision using cell tracking annotation and image registration improves cell segmentation,” in 2022 Eleventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Salzburg, Austria, 2022 (IEEE, 2022), pp. 1–5. https://doi.org/10.1109/ipta54936.2022.9784140

E. A. Arifulin, D. V. Sorokin, A. V. Tvorogova, M. A. Kurnaeva, Ya. R. Musinova, O. A. Zhironkina, S. A. Golyshev, S. S. Abramchuk, Ye. S. Vassetzky, and E. V. Sheval, “Heterochromatin restricts the mobility of nuclear bodies,” Chromosoma 127, 529–537 (2018). https://doi.org/10.1007/s00412-018-0683-8CrossRefPubMed

L. A. Blagonravov, S. N. Skovorod’ko, A. S. Krylov, L. A. Orlov, V. A. Alekseev, and E. E. Shpilrain, “Phase transition in liquid cesium near 590 K,” J. Non-Cryst. Solids 277, 182–187 (2000). https://doi.org/10.1016/s0022-3093(00)00315-xADSCrossRef

J. Borovec, J. Kybic, I. Arganda-Carreras, D. V. Sorokin, G. Bueno, A. V. Khvostikov, S. Bakas, E. I. Chang, S. Heldmann, K. Kartasalo, L. Latonen, J. Lotz, M. Noga, S. Pati, K. Punithakumar, P. Ruusuvuori, A. Skalski, N. Tahmasebi, M. Valkonen, L. Venet, Yi. Wang, N. Weiss, M. Wodzinski, Yu. Xiang, Ya. Xu, Ya. Yan, P. Yushkevich, S. Zhao, and A. Munoz-Barrutia, “ANHIR: Automatic non-rigid histological image registration challenge,” IEEE Trans. Med. Imaging 39, 3042–3052 (2020). https://doi.org/10.1109/tmi.2020.2986331CrossRefPubMedPubMedCentral

C. Castilla, M. Maska, D. V. Sorokin, E. Meijering, and C. Ortiz-De-Solorzano, “3-D quantification of filopodia in motile cancer cells,” IEEE Trans. Med. Imaging 38, 862–872 (2019). https://doi.org/10.1109/tmi.2018.2873842CrossRefPubMed

K.-L. Chung, Yo.-H. Huang, Sh.-M. Shen, A. S. Krylov, D. V. Yurin, and E. V. Semeikina, “Efficient sampling strategy and refinement strategy for randomized circle detection,” Pattern Recognit. 45, 252–263 (2012). https://doi.org/10.1016/j.patcog.2011.07.004ADSCrossRef

10.

Yo. Ding, R. Deng, X. Xie, X. Xu, Ya. Zhao, X. Chen, and A. S. Krylov, “No-reference stereoscopic image quality assessment using convolutional neural network for adaptive feature extraction,” IEEE Access 6, 37595–37603 (2018). https://doi.org/10.1109/access.2018.2851255CrossRef

11.

A. A. Dovganich, A. V. Khvostikov, A. S. Krylov, and L. E. Parolina, “Automatic quality control in lung X‑ray imaging with deep learning,” Comput. Math. Model. 32, 276–285 (2021). https://doi.org/10.1007/s10598-021-09539-6CrossRef

12.

A. A. Dovganich, A. V. Khvostikov, Ya. A. Pchelintsev, A. A. Krylov, Yo. Ding, and M. C. Q. Farias, “Automatic out-of-distribution detection methods for improving the deep learning classification of pulmonary X-ray images,” J. Image Graphics 10, 56–63 (2022). https://doi.org/10.18178/joig.10.2.56-63CrossRef

13.

A. A. Dovganich and A. S. Krylov, “A nonlocal image denoising algorithm using the structural similarity metric,” Program. Comput. Software 45, 141–146 (2019). https://doi.org/10.1134/s0361768819040029CrossRef

14.

A. Dovganich, A. Krylov, A. Nasonov, and N. Makhneva, “Epidermis area detection for immunofluorescence microscopy,” Proc. SPIE 10615, 1061522 (2017). https://doi.org/10.1117/12.2302591

15.

A. Dogvanich, N. Mamaev, A. Krylov, and N. Makhneva, “Dermatological image denoising using adaptive henlm method,” ISPRS J. Photogrammetry Remote Sensing 42 (2/W12), 47–52 (2019).

16.

A. A. Dovganich, A. V. Nasonov, A. S. Krylov, and N. V. Makhneva, “Ridge-based method for pemphigus diagnosis on immunofluorescence images,” in Proc. 26th Int. Conf. on Computer Graphics and Vision GraphiCon-2016, Nizhny Novgorod, 2016 (Inst. Fiz.-Tekh. Informatiki, Protvino, Moscow oblast, 2016), pp. 170–174.

17.

F. Guryanov and A. Krylov, “Fast medical image registration using bidirectional empirical mode decomposition,” Signal Process.: Image Commun. 59, 12–17 (2017). https://doi.org/10.1016/j.image.2017.04.003CrossRef

18.

F. A. Guryanov and A. S. Krylov, “Optimization method for cell image registration,” Program. Comput. Software 44, 266–270 (2018). https://doi.org/10.1134/s0361768818040072CrossRef

19.

A. D. Gusev, A. V. Nasonov, and A. S. Krylov, “Fast parallel grid warping-based image sharpening method,” Program. Comput. Software 43, 230–233 (2017). https://doi.org/10.1134/s036176881704003xMathSciNetCrossRef

20.

L. Jiale, Y. Sun, S. Luo, Z. Zhu, H. Dai, A. S. Krylov, Y. Ding, and L. Shao, “P2V-RCNN: Point to voxel feature learning for 3D object detection from point clouds,” IEEE Access 9, 98260 (2021). https://doi.org/10.1109/ACCESS.2021.3094562CrossRef

21.

V. E. Karnaukhov, A. S. Krylov, Yo. Ding, and M. C. Q. Farias, “Hybrid method for biomedical image poisson denoising,” in Proceedings of the 2020 5th International Conference on Biomedical Signal and Image Processing, Suzhou, China, 2020 (Association for Computing Machinery, New York, 2020), pp. 32–36. https://doi.org/10.1145/3417519.3417553

22.

N. A. Khanina, E. V. Semeikina, and D. V. Yurin, “Scale-space color blob and ridge detection,” Pattern Recognit. Image Anal. 22, 221–227 (2012). https://doi.org/10.1134/s1054661812010221CrossRef

23.

A. Khvostikov, K. Aderghal, J. Benois-Pineau, A. Krylov, and G. Catheline, “3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies,” arXiv Preprint (2018). https://doi.org/https://doi.org/10.48550/arXiv.1801.05968

24.

A. Khvostikov, K. Aderghal, A. Krylov, G. Catheline, and J. Benois-Pineau, “3D inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer’s disease diagnostics,” arXiv Preprint (2018). https://doi.org/https://doi.org/10.48550/arXiv.1809.03972

25.

A. Khvostikov, A. Krylov, J. Kamalov, and A. Megroyan, “Influence of ultrasound despeckling on the liver fibrosis classification,” in 2015 Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Orleans, France, 2015 (IEEE, 2015), pp. 440–445. https://doi.org/10.1109/ipta.2015.7367183

26.

A. Khvostikov, A. Krylov, J. Kamalov, and A. Megroyan, “Ultrasound despeckling by anisotropic diffusion and total variation methods for liver fibrosis diagnostics,” Signal Process.: Image Commun. 59, 3–11 (2017). https://doi.org/10.1016/j.image.2017.09.005CrossRef

27.

A. Khvostikov, A. Krylov, I. Mikhailov, O. Kharlova, N. Oleynikova, and P. Malkov, “Automatic mucous glands segmentation in histological images,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 2, 103–109 (2019). https://doi.org/10.5194/isprs-archivesXLII-2-W12-103-2019CrossRef

28.

A. V. Khvostikov, A. S. Krylov, I. A. Mikhailov, and P. G. Malkov, “Trainable active contour model for histological image segmentation,” Sci. Visualization 11 (3), 64–75 (2019). https://doi.org/10.26583/sv.11.3.06CrossRef

29.

A. Khvostikov, A. S. Krylov, I. Mikhailov, and P. Malkov, “CNN assisted hybrid algorithm for medical images segmentation,” in Proc. 2020 5th Int. Conf. on Biomedical Signal and Image Processing, Suzhou, China, 2020 (Association for Computing Machinery, New York, 2020), pp. 14–19. https://doi.org/10.1145/3417519.3417557

30.

A. Khvostikov, A. Krylov, I. Mikhailov, P. Malkov, and N. Danilova, “Tissue type recognition in whole slide histological images,” CEUR Workshop Proc. 3027, 50 (2021). https://doi.org/10.20948/graphicon-2021-3027-496-507CrossRef

31.

A. Yu. Kondratiev, H. Yaginuma, Ya. Okada, and D. V. Sorokin, “A method for automatic tracking of cell nuclei in 2D epifluorescence microscopy image sequences,” in 2018 Eighth Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Xi’an, China, 2018 (IEEE, 2018), pp. 1–6. https://doi.org/10.1109/ipta.2018.8608156

32.

A. Yu. Kondratiev, H. Yaginuma, Ya. Okada, A. S. Krylov, and D. V. Sorokin, “A method for automatic tracking of cell nuclei with weakly-supervised mitosis detection in 2D microscopy image sequences,” in Proc. 2020 5th Int. Conf. on Biomedical Signal and Image Processing, Suzhou, China, 2020 (Association for Computing Machinery, 2020), pp. 67–73. https://doi.org/10.1145/3417519.3417558

33.

A. Yu. Kondrat’ev and D. V. Sorokin, “Automatic detection of laser-induced structures in live cell fluorescent microscopy images using snakes with geometric constraints,” in 2016 23rd Int. Conf. on Pattern Recognition (ICPR), Cancun, Mexico, 2016 (IEEE, 2016), pp. 326–331. https://doi.org/10.1109/icpr.2016.7899655

34.

D. Kononykhin, V. Berg, A. Krylov, and D. Sorokin, “A method for actin filament tracking in fluorescent microscopy images,” CEUR Workshop Proc. 2744, 37 (2020). https://doi.org/10.51130/graphicon-2020-2-3-37CrossRef

35.

D. Kortchagine and A. Krylov, “Image database retrieval by fast Hermite projection method,” in Proc. 15th Int. Conf. on Computer Graphics and Vision GraphiCon-2005 (Novosibirsk, 2005), pp. 137–140.

36.

A. Krylov, F. Guryanov, N. Mamaev, and D. Yurin, “Fast estimation of downsampling factor for biomedical image registration,” in Proc. 2018 3rd Int. Conf. on Biomedical Imaging, Signal Processing, Bari, Italy, 2018 (Association for Computing Machinery, New York, 2018), pp. 36–40. https://doi.org/10.1145/3288200.3288203

37.

A. Krylov, V. Karnaukhov, N. Mamaev, and A. Khvos-tikov, “Hybrid method for biomedical image denoising,” in Proc. 2019 4th Int. Conf. on Biomedical Imaging, Signal Processing, Nagoya, Japan, 2019 (Association for Computing Machinery, New York, 2019), pp. 60–64. https://doi.org/10.1145/3366174.3366184

38.

A. Krylov and D. Korchagin, “Fast Hermite Projection Method,” in Image Analysis and Recognition. ICIAR 2006, Ed. by A. Campilho and M. Kamel, Lecture Notes in Computer Science, Vol. 4141 (Springer, Berlin, 2006), pp. 329–338. https://doi.org/10.1007/11867586_31CrossRef

39.

D. N. Kortchagine and A. S. Krylov, “Projection filtering in image processing,” in Proc. 10th Int. Conf. on Computer Graphics and Vision GraphiCon-2000 (Moscow, 2000), pp. 42–45.

40.

A. S. Krylov, A. V. Kutovoi, and K. L. Wee, “Texture parameterization with Hermite functions,” in Proc. 12th Int. Conf. on Computer Graphics and Vision GraphiCon-2002 (Nizhny Novgorod, 2002), pp. 190–194.

41.

A. S. Krylov and A. V. Liakishev, “Numerical projection method for inverse Fourier transform and its application,” Numer. Funct. Anal. Optim. 21, 205–216 (2000). https://doi.org/10.1080/01630560008816949MathSciNetCrossRef

42.

A. Krylov and A. Nasonov, “Adaptive total variation deringing method for image interpolation,” in 2008 15th IEEE Int. Conf. on Image Processing, San Diego, Calif., 2008 (IEEE, 2008), pp. 2608–2611. https://doi.org/10.1109/icip.2008.4712328

43.

A. S. Krylov and A. V. Nasonov, “Adaptive image deblurring with ringing control,” in 2009 Fifth Int. Conf. on Image and Graphics, Xi’an, Shanxi, China, 2009 (IEEE, 2009), pp. 72–75. https://doi.org/10.1109/icig.2009.136

44.

A. S. Krylov and A. V. Nasonov, “3D image sharpening by grid warping,” in Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015, Ed. by X. He, Lecture Notes in Computer Science, Vol. 9242 (Springer, Cham, 2015), pp. 441–450. https://doi.org/10.1007/978-3-319-239897_45CrossRef

45.

A. S. Krylov and A. V. Nasonov, “Adaptive image deblurring with ringing control,” in 2009 Fifth Int. Conf. on Image and Graphics, Moscow, 2009 (IEEE, 2009), pp. 163–165. https://doi.org/10.1109/icig.2009.136

46.

A. Krylov, A. Nasonov, K. Chesnakov, A. Nasonova, S. O. Jin, U. Kang, and S. M. Park, “Vessel preserving CNN-based image resampling of retinal images,” in Image Analysis and Recognition. ICIAR 2018, Ed. by A. Campilho, F. Karray, and B. ter Haar Romeny, Lecture Notes in Computer Science, Vol. 10882 (Springer, Cham, 2018), pp. 589–597. https://doi.org/10.1007/978-3-319-93000-8_67CrossRef

47.

A. Krylov, A. Nasonov, and Ya. Pchelintsev, “Single parameter post-processing method for image deblurring,” in 2017 Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Montreal, 2017 (IEEE, 2017), pp. 1–7. https://doi.org/10.1109/ipta.2017.8310093

48.

A. Krylov, A. Nasonov, A. Razgulin, and T. Romanenko, “A post-processing method for 3D fundus image enhancement,” in 2016 IEEE 13th Int. Conf. on Signal Processing (ICSP), Chengdu, China, 2016 (IEEE, 2016), pp. 49–52. https://doi.org/10.1109/icsp.2016.7877794

49.

A. S. Krylov, A. V. Nasonov, and D. V. Sorokin, “Face image super-resolution from video data with nonuniform illumination,” in 18th Int. Conf. on Computer Graphics GraphiCon-2008 (2008), pp. 150–155.

50.

A. S. Krylov, A. S. Nasonov, and O. S. Ushmaev, “Image super-resolution using fast deconvolution,” in Proc. 9th Conf. on Pattern Recognition and Image Analysis: New Information Technologies (2008), Vol. 1, pp. 362–364.

51.

A. S. Krylov, A. V. Nasonova, and A. A. Nasonov, “Image enhancement by non-iterative grid warping,” Pattern Recognit. Image Anal. 26, 161–164 (2016). https://doi.org/10.1134/s1054661816010132CrossRef

52.

A. Krylov, M. Penkin, N. Mamaev, and A. Khvostikov, “How to choose adaptively parameters of image denoising methods,” in 2019 Ninth Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Istanbul, 2019 (IEEE, 2019), pp. 1–6. https://doi.org/10.1109/IPTA.2019.8936109

53.

A. S. Krylov, J. F. Poliakoff, and M. Stockenhuber, “An Hermite expansion method for EXAFS data treatment and its application to Fe K-edge spectra,” Phys. Chem. Chem. Phys. 2, 5743–5749 (2000). https://doi.org/10.1039/b004346jCrossRef

54.

A. S. Krylov, D. V. Sorokin, D. V. Yurin, and E. V. Semeikina, “Use of color information for keypoints detection and descriptors construction,” in Intelligent Science and Intelligent Data Engineering, Lecture Notes in Computer Science, Vol. 7202 (Springer, Berlin, 2012), pp. 389–396. https://doi.org/10.1007/978-3-642-31919-8_50CrossRef

55.

A. V. Kvostikov, A. S. Krylov, and U. R. Kamalov, “Ultrasound image texture analysis for liver fibrosis stage diagnostics,” Program. Comput. Software 41, 273–278 (2015). https://doi.org/10.1134/s0361768815050059MathSciNetCrossRef

56.

A. Lukin, A. Krylov, and A. Nasonov, “Image interpolation by super-resolution,” in Proc. 16th Int. Conf. GraphiCon-2006 (2006), pp. 239–242.

57.

D. A. Lyukov, A. S. Krylov, and V. A. Lukshin, “Total generalized variation method for deconvolution-based CT brain perfusion,” CEUR Workshop Proc. 2485, 136–139 (2019). https://doi.org/10.30987/graphicon-2019-2-136-139

58.

D. A. Lyukov, A. S. Krylov, V. A. Lukshin, and D. Yu. Usachev, “Projection method for deconvolution-based CT brain perfusion,” Program. Comput. Software 46, 217–222 (2020). https://doi.org/10.1134/s0361768820030056MathSciNetCrossRef

59.

N. V. Mamaev and A. S. Krylov, “Using anisotropic diffusion in the multiscale ridge detection method,” Comput. Math. Model. 30, 191–197 (2019). https://doi.org/10.1007/s10598-019-09446-xMathSciNetCrossRef

60.

N. Mamaev, A. Krylov, and D. Yurin, “Choice of the regularization parameter for total variation image denoising using no-reference metric,” in Proc. Int. Conf. Interfaces and Human Computer Interaction 2018; Game and Entertainment Technologies 2018; and Computer Graphics, Visualization, Computer Vision and Image Processing, Ed. by K. Blashki and Yi. Xiao (IADIS Press, 2018), pp. 253–260. https://doi.org/10.33965/cgv2019

61.

N. V. Mamaev, A. S. Lukin, and D. V. Yurin, “HeNLM-LA: A locally adaptive non-local means algorithm based on hermite functions expansion,” Program. Comput. Software 40, 199–207 (2014). https://doi.org/10.1134/s0361768814040070MathSciNetCrossRef

62.

N. V. Mamaev, A. S. Lukin, and D. V. Yurin, “HeNLM-LA3D: A three-dimensional locally adaptive Hermite functions expansion based non-local means algorithm for CT applications,” Pattern Recognit. Image Anal. 25, 658–668 (2015). https://doi.org/10.1134/s105466181504015xCrossRef

63.

N. Mamaev, D. Yurin, and A. Krylov, “Image ridge denoising using no-reference metric,” in Advanced Concepts for Intelligent Vision Systems. ACIVS 2017, Ed. by J. Blanc-Talon, R. Penne, W. Philips, D. Popescu, and P. Scheunders, Lecture Notes in Computer Science, 2017, Vol. 10617, pp. 591–601. https://doi.org/10.1007/978-3-319-703534_50

64.

N. Mamaev, D. Yurin, and A. Krylov, “Choice of the parameter for BM3D denoising algorithm using no- reference metric,” in 2018 7th Eur. Workshop on Visual Information Processing (EUVIP), Tampere, Finland, 2018 (IEEE, 2018), pp. 1–6. https://doi.org/10.1109/euvip.2018.8611679

65.

N. V. Mamaev, D. V. Yurin, and A. S. Krylov, “Finding the parameters of a nonlinear diffusion denoising method by ridge analysis,” Comput. Math. Model. 29, 334–343 (2018). https://doi.org/10.1007/s10598-018-9413-6MathSciNetCrossRef

66.

M. Maå¡ka, T. Necasova, D. Wiesner, D. V. Sorokin, I. Peterlãk, V. Ulman, and D. Svoboda, “Toward robust fully 3D filopodium segmentation and tracking in time-lapse fluorescence microscopy,” in 2019 IEEE Int. Conf. on Image Processing, Taipei, 2019 (IEEE, 2019), pp. 819–823. https://doi.org/10.1109/ICIP.2019.8803721

67.

M. Najafi, A. Krylov, and D. Kortchagine, “Image deblocking with 2-D Hermite transform,” in Proceedings of the 13th Int. Conf. on Computer Graphics and Vision GraphiCon-2003 (Moscow, 2003), pp. 180–183.

68.

A. Nasonov, K. Chesnakov, and A. Krylov, “Convolutional neural networks based image resampling with noisy training set,” in 2016 IEEE 13th Int. Conf. on Signal Processing (ICSP), Chengdu, China, 2016 (IEEE, 2016), pp. 62–66. https://doi.org/10.1109/icsp.2016.7877797

69.

A. Nasonov, K. Chesnakov, and A. Krylov, “CNN based retinal image upscaling using zero component analysis,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci.-ISPRS Arch. 24 (2W4), 27–31 (2017). https://doi.org/10.5194/isprsarchives-XLII-2-W4-27-2017

70.

A. V. Nasonov and A. S. Krylov, “Adaptive image deringing,” in Proceedings of the 19th Int. Conf. on Computer Graphics and Vision GraphiCon-2009 (Moscow, 2009), pp. 151–154.

71.

A. V. Nasonov and A. S. Krylov, “Scale-space method of image ringing estimation,” in 2009 16th IEEE Int. Conf. on Image Processing (ICIP), Cairo, 2009 (IEEE, 2009), pp. 2794–2797. https://doi.org/10.1109/icip.2009.5414172

72.

A. V. Nasonov and A. S. Krylov, “Basic edges metrics for image deblurring,” in Proceedings of the 10th Conference on Pattern Recognition and Image Analysis: New Information Technologies 1, 243–246 (2010).

73.

A. V. Nasonov and A. S. Krylov, “Fast super-resolution using weighted median filtering,” in 20th Int. Conf. on Pattern Recognition, Istanbul, 2010 (IEEE, 2010), pp. 2230–2233. https://doi.org/10.1109/icpr.2010.546

74.

A. Nasonov and A. Krylov, “An improvement of BM3D image denoising and deblurring algorithm by generalized total variation,” in 2018 7th Eur. Workshop on Visual Information Processing (EUVIP), Tampere, Finland, 2018 (IEEE, 2018), pp. 1–4. https://doi.org/10.1109/euvip.2018.8611693

75.

A. Nasonov and A. Krylov, “Image sharpening by grid warping with curvature analysis,” in 2019 15th Int. Conf. on Signal-Image Technology & Internet-Based Systems (SITIS), Sorrento, Italy, 2019 (IEEE, 2019), pp. 262–267. https://doi.org/10.1109/sitis.2019.00051

76.

A. Nasonov, A. Krylov, and K. Chesnakov, “An image resampling method using combined directional kernels,” in 2016 6th Eur. Workshop on Visual Information Processing (EUVIP), Marseille, France, 2016 (IEEE, 2016), pp. 1–5. https://doi.org/10.1109/euvip.2016.7764602

77.

A. Nasonov, A. Krylov, and A. Lukin, “Post-processing by total variation quasi-solution method for image interpolation,” in Proc. 17th Int. Conf. on Computer Graphics GraphiCon-2007 (2007), pp. 178–181.

78.

A. Nasonov, A. Krylov, and D. Lyukov, “Image sharpening with blur map estimation using convolutional neural network,” ISPRS - Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 42 (2/W12), 161–166 (2019). https://doi.org/10.5194/isprsarchives-XLII-2-W12-161-201

79.

A. V. Nasonov, A. S. Krylov, X. Yu. Petrova, and M. N. Rychagov, “Edge-directional interpolation algorithm using structure tensor,” Electron. Imaging 28 (15), 1–4 (2016). https://doi.org/10.2352/issn.2470-1173.2016.15.ipas-026CrossRef

80.

A. V. Nasonov, N. V. Mamaev, O. S. Volodina, and A. S. Krylov, “Automatic choice of denoising parameter in Perona–Malik model,” CEUR Workshop Proc. 2485, 144–147 (2019). https://doi.org/10.30987/graphicon-2019-2-144-147CrossRef

81.

A. Nasonov, A. Nasonova, and A. Krylov, “Edge width estimation for defocus map from a single image,” in Advanced Concepts for Intelligent Vision Systems, Ed. by S. Battiato, J. Blanc-Talon, G. Gallo, W. Philips, D. Popescu, and P. Scheunders, Lecture Notes in Computer Science, Vol. 9386 (Springer, Cham, 2015), pp. 15–22. https://doi.org/10.1007/978-3-319-25903-1_2CrossRef

82.

A. Nasonov, Ya. Pchelintsev, and A. Krylov, “Grid warping postprocessing for linear motion blur in images,” in 2018 7th European Workshop on Visual Information Processing (EUVIP), Tampere, Finland, 2018 (IEEE, 2018), pp. 1–5. https://doi.org/10.1109/euvip.2018.8611700

83.

A. V. Nasonov, O. S. Volodina, and A. S. Krylov, “Non-linear multi-frame image denoising using weighted nuclear norm minimization,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 44-2 (W1-2021), 167–170 (2021). https://doi.org/10.5194/isprsarchives-XLIV-2-W1-2021-167-2021

84.

A. Nasonova and A. Krylov, “Deblurred images post-processing by Poisson warping,” IEEE Signal Process. Lett. 22, 417–420 (2014). https://doi.org/10.1109/lsp.2014.2361492ADSCrossRef

85.

A. Nasonova, A. Nasonov, A. Krylov, I. Pechenko, A. Umnov, and N. Makhneva, “Image warping in dermatological image hair removal,” in Image Analysis and Recognition. ICIAR 2014, Ed. by A. Campilho and M. Kamel, Lecture Notes in Computer Science, Vol. 8815 (Springer, Cham, 2014), pp. 159–166. https://doi.org/10.1007/978-3-319-11755-3_18CrossRef

86.

N. Oleynikova, A. Khvostikov, A. Krylov, I. Mikhailov, O. Kharlova, N. Danilova, P. Malkov, N. Ageykina, and E. Fedorov, “Automatic glands segmentation in histological images obtained by endoscopic biopsy from various parts of the colon,” Endoscopy 51, S6–S7 (2019). https://doi.org/10.1055/s-0039-1681188

87.

E. Pavelyeva, “Hermite projection phase-only correlation method in iris key points,” in The 22nd Int. Conf. on Computer Graphics and Vision GraphiCon-2012 (2012), pp. 128–132.

88.

E. Pavelyeva, “The search for matches between the iris key points using Hermite projection phase-only correlation method,” Syst. Means Inf. 23, 74–88 (2013). https://doi.org/10.14357/08696527130206CrossRef

89.

E. Pavelyeva, “Image processing and analysis based on the use of phase information,” Komp’yuternaya Opt. 42, 1022–1034 (2018). https://doi.org/10.18287/2412-6179-2018-42-6-1022-1034ADSCrossRef

90.

E. A. Pavelyeva and A. S. Krylov, “An adaptive algorithm of iris image key points detection,” in GraphiCon-2010 (2010), pp. 320–323.

91.

E. A. Pavelyeva and A. S. Krylov, “Image reconstruction from phase using Hermite projection method,” in 11th Int. Conf. Pattern Recognition and Image Analysis: New Information Technologies (PRIA-11-2013) (2013), pp. 296–299.

92.

E. A. Pavelyeva and A. S. Krylov, “Synthesis of phase and magnitude of images by Hermite projection method,” Pattern Recognit. Image Anal. 25, 187–192 (2015). https://doi.org/10.1134/s1054661815020200CrossRef

93.

I. A. Pchelintsev, A. V. Nasonov, and A. S. Krylov, “Regularization methods in the analysis of a series of scintillation fluorescence microscopy images,” Comput. Math. Model. 32, 111–119 (2021). https://doi.org/10.1007/s10598-021-09520-3MathSciNetCrossRef

94.

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

95.

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

96.

M. Penkin, A. Krylov, and A. Khvostikov, “Attention-based convolutional neural network for MRI Gibbs-ringing artifact suppression,” CEUR Workshop Proc. 2744, 1–12 (2020). https://doi.org/10.51130/graphicon-2020-2-3-34CrossRef

97.

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/s0361768821030087MathSciNetCrossRef

98.

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

99.

M. A. Protsenko and E. A. Pavelyeva, “Iris image key points descriptors based on phase congruency,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 42, 167–171 (2019). https://doi.org/10.5194/isprs-archives-xlii-2-w12-167-2019

100.

M. A. Protsenko and E. A. Pavelyeva, “Image key point matching by phase congruency,” Comput. Math. Model. 32, 297–304 (2021). https://doi.org/10.1007/s10598-021-09532-zMathSciNetCrossRef

101.

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/s1054661822030324CrossRef

102.

E. Safronova and E. Pavelyeva, “Unsupervised palm vein image segmentation,” CEUR Workshop Proc. 2744, 40 (2020). https://doi.org/10.51130/graphicon-2020-2-3-40CrossRef

103.

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

104.

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

105.

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.

106.

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

107.

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/s1054661815010186CrossRef

108.

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

109.

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.

110.

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/s1054661812020162CrossRef

111.

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_29CrossRef

112.

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.2017.2734169CrossRefPubMed

113.

D. V. Sorokin, I. Peterlik, V. Ulman, D. Svoboda, and M. Maska, “Model-based generation of synthetic 3D time-lapse sequences of motile cells with growing filopodia,” in 2017 IEEE 14th Int. Symp. on Biomedical Imaging (ISBI 2017), Melbourne, 2017 (IEEE, 2017), pp. 822–826. https://doi.org/10.1109/isbi.2017.7950644

114.

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.2018.2845884CrossRefPubMed

115.

S. Stanković, I. Orović, and A. Krylov, “Two-dimensional Hermite S-method for high-resolution inverse synthetic aperture radar imaging applications,” IET Signal Process. 4, 352–362 (2010). https://doi.org/10.1049/iet-spr.2009.0060CrossRef

116.

S. Stanković, I. Orović, and A. Krylov, “Video frames reconstruction based on time-frequency analysis and hermite projection method,” EURASIP J. Adv. Signal Process. 2010, 970105 (2010). https://doi.org/10.1155/2010/970105

117.

M. Storozhilova, A. Lukin, D. Yurin, and V. Sinitsyn, “2.5D Extension of neighborhood filters for noise reduction in 3D medical CT images,” in Transactions on Computational Science XIX, Ed. by M. L. Gavrilova, C. J. K. Tan, and A. Konushin, Lecture Notes in Computer Science, Vol. 7870 (Springer, Berlin, 2013), pp. 1–16. https://doi.org/10.1007/978-3-642-39759-2_1CrossRef

118.

G. Sun, B. Shi, X. Chen, A. S. Krylov, and Yo. Ding, “Learning local quality-aware structures of salient regions for stereoscopic images via deep neural networks,” IEEE Trans. Multimedia 22, 2938–2949 (2020). https://doi.org/10.1109/tmm.2020.2965461CrossRef

119.

D. I. Sungatullina, A. S. Krylov, and D. N. Fedorov, “Fast registration algorithms for histological images,” Nauchnaya Vizualizatsiya 6 (4), 61–71 (2014).

120.

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

121.

V. Tikhonova and E. Pavelyeva, “Hybrid iris segmentation method based on CNN and principal curvatures,” CEUR Workshop Proc. 2744, 31 (2020). https://doi.org/10.51130/graphicon-2020-2-3-31CrossRef

122.

A. V. Umnov and A. S. Krylov, “Sparse approach to image ringing detection and suppression,” Pattern Recognit. Image Anal. 27, 754–762 (2017). https://doi.org/10.1134/s1054661817040186CrossRef

123.

A. V. Umnov, A. S. Krylov, and A. V. Nasonov, “Ringing artifact suppression using sparse representation,” in Advanced Concepts for Intelligent Vision Systems, Ed. by S. Battiato, J. Blanc-Talon, G. Gallo, W. Philips, D. Popescu, and P. Scheunders, Lecture Notes in Computer Science, Vol. 9386 (Springer, Cham, 2015), pp. 35–45. https://doi.org/10.1007/978-3-319-25903-1_4CrossRef

124.

A. V. Umnov, A. V. Nasonov, A. S. Krylov, and D. Yong, “Sparse method for ringing artifact detection,” in 2014 12th Int. Conf. on Signal Processing (ICSP), Hangzhou, China, 2014 (IEEE, 2014), pp. 662–667. https://doi.org/10.1109/icosp.2014.7015086

125.

O. S. Volodina, A. V. Nasonov, and A. S. Krylov, “Choice of parameters in the weighted nuclear norm method for image denoising,” Comput. Math. Model. 31, 402–409 (2020). https://doi.org/10.1007/s10598-020-09500-zMathSciNetCrossRef

126.

A. Yatchenko and A. Krylov, “Cross-frame ultrasonic color Doppler flow heart image unwrapping,” in Functional Imaging and Modeling of the Heart, Ed. by H. van Assen, P. Bovendeerd, and T. Delhaas, Lecture Notes in Computer Science, Vol. 9126 (Springer, Cham, 2015), pp. 265–272. https://doi.org/10.1007/978-3-319-20309-6_31CrossRef

127.

A. M. Yatchenko, A. S. Krylov, A. V. Gavrilov, and I. V. Arkhipov, “Graph-cut based antialiasing for Doppler ultrasound color flow medical imaging,” in 2011 Visual Communications and Image Processing (VCIP), Tainan, Taiwan, 2011 (IEEE, 2011), pp. 1–4. https://doi.org/10.1109/vcip.2011.6115923

128.

A. M. Yatchenko, A. S. Krylov, A. V. Gavrilov, and I. V. Arkhipov, “Building a three-dimensional dynamic model of left cardiac ventricle from ultrasonic data,” Pattern Recognit. Image Anal. 22, 483–488 (2012). https://doi.org/10.1134/s1054661812030091CrossRef

129.

A. Yatchenko, A. Krylov, A. Gavrilov, V. Sandrikov, and T. Kulagina, “Image preprocessing for color Doppler flow antialiasing using power and complex phase data,” in 12th International Conference on Signal Processing (ICSP), Hangzhou, China, 2014 (IEEE, 2014), pp. 1072–1076. https://doi.org/10.1109/ICOSP.2014.7015168

130.

A. M. Yatchenko, A. S. Krylov, and A. V. Nasonov, “Deringing of MRI medical images,” Pattern Recognit. Image Anal. 23, 541–546 (2013). https://doi.org/10.1134/s1054661813040184CrossRef

131.

A. M. Yatchenko, A. S. Krylov, V. A. Sandrikov, and T. Yu. Kulagina, “Regularizing method for phase antialiasing in color doppler flow mapping,” Neurocomputing 139, 77–83 (2014). https://doi.org/10.1016/j.neucom.2013.09.060CrossRef

Titel: Image Analysis and Enhancement: General Methods and Biomedical Applications
verfasst von: A. S. Krylov
A. V. Nasonov
D. V. Sorokin
A. V. Khvostikov
E. A. Pavelyeva
Ya. A. Pchelintsev
Publikationsdatum: 01.12.2023
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 4/2023
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661823040235

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Formation and Development of Image Analysis and Pattern Recognition in the USSR, the Republic of Belarus, and the Russian Federation

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