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Algorithm for post-processing of tomography images to calculate the dimension-geometric features of porous structures
M.V. Chukalina 1,2, A.V. Khafizov 1, V.V. Kokhan 2,3, A.V. Buzmakov 1,2, R.A. Senin 4, V.I. Uvarov 5, M.V. Grigoriev 6

FSRC "Crystallography and Photonics" RAS, 119333, Russia, Moscow, Leninskiy Prospekt, 59,
Smart Engines LLC, 117312, Russia, Moscow, 60-letiya Oktyabrya Ave, 9,
Institute for Information Transmission Problems RAS, 127051, Russia, Moscow, Karetny per. 19, build. 1,
NRC Kurchatov Institute, 123098, Russia, Moscow, Ploshchad' Akademika Kurchatova, 1, b. 113,
Institute of Structural Macrokinetics and Materials Science RAS,
142432, Russia, Chernogolovka, Ulitsa Akademika Osip'yana, 8,
Institute of Microelectronics Technology and High-Purity Materials of the Russian Academy of Sciences,
142432, Russia, Chernogolovka, Ulitsa Akademika Osip'yana, 6

 PDF, 1427 kB

DOI: 10.18287/2412-6179-CO-781

Pages: 110-121.

Full text of article: English language.

An algorithm for post-processing of the grayscale 3D computed tomography (CT) images of porous structures with the automatic selection of filtering parameters is proposed. The determination of parameters is carried out on a representative part of the image under analysis. A criterion for the search for optimal filtering parameters based on the count of "levitating stone" voxels is described. The stages of CT image filtering and its binarization are performed sequentially. Bilateral and anisotropic diffuse filtering is implemented; the Otsu method for unbalanced classes is chosen for binarization. Verification of the proposed algorithm was carried out on model data. To create model porous structures, we used our image generator, which implements the function of anisotropic porous structures generation. Results of the post-processing of real CT images containing noise and reconstruction artifacts by the proposed method are discussed.

postprocessing of CT-images, CT images of porous structures, representative volume element, anisotropic porous structures.

Chukalina MV, Khafizov AV, Kokhan VV, Buzmakov AV, Senin RA, Uvarov VI, Grigoriev MV. Algorithm for post-processing of tomography images to calculate the dimension-geometric features of porous structures. Computer Optics 2021; 45(1): 110-121. DOI: 10.18287/2412-6179-CO-781.

This work was partly supported by the RF Ministry of Science and Higher Education within the State assignment of the FSRC "Crystallography and Photonics" RAS (computed tomography measurements and data analysis) and the Russian Foundation for Basic Research (RFBR) under projects Nos. 18-29-26019 and 19-01-00790 (algorithms development).


  1. Wang SY, Huang YB, Pereira V, Gryte CC. Application of computed tomography to oil recovery from porous media. Appl Opt 1985; 24(23): 4021-4027. DOI: 10.1364/AO.24.004021.
  2. Degruyter W, Burgisser A, Bachmann O, Malaspinas O. Synchrotron X-ray microtomography and lattice Boltzmann simulations of gas flow through volcanic pumices. Geosphere 2010; 6(5): 470-481. DOI: 10.1130/GES00555.1.
  3. Coléou C, Lesaffre B, Brzoska J-B, Ludwig W, Boller E. Three-dimensional snow images by X-ray microtomography. Ann Glaciol 2001; 32: 75-81. DOI: 10.3189/172756401781819418.
  4. Maira E, Colombo P, Adrien J, Babout L, Biasetto L. Characterization of the morphology of cellular ceramics by 3D image processing of X-ray tomography. J Eur Ceram Soc 2007; 27(4): 1973-1981. DOI: 10.1016/j.jeurceramsoc.2006.05.097.
  5. Egorov AA, Fedotov AY, Mironov AV, Komlev VS, Popov VK, Zobkov YV. 3D printing of mineral–polymer bone substitutes based on sodium alginate and calcium phosphate. Beilstein J Nanotechnol 2016; 7(1): 1794-1799. DOI: 10.3762/bjnano.7.172.
  6. Seong H, Choi S, Matusik KE, Kastengren AL, Powell ChF. 3D pore analysis of gasoline particulate filters using X-ray tomography: impact of coating and ash loading. J Mater Sci 2019; 54(8): 6053-6065. DOI: 10.1007/s10853-018-03310-w.
  7. Jones AC, Arns ChH, Sheppard AP, Hutmacher DW, Milthorpe BK, Knackstedt MA. Assessment of bone ingrowth into porous biomaterials using MICRO-CT. Biomaterials 2007; 28(15): 2491-2504. DOI: 10.1016/j.biomaterials.2007.01.046.
  8. Wang SY, Ayral S, Gryte CC. Computer-assisted tomography for the observation of oil displacement in porous media. Society Petroleum Engineers Journal 1984; 24(1): 53-55. DOI: 10.2118/11758-PA.
  9. Lymberopoulos DP, Payatakes AC. Derivation of topological, geometrical, and correlational properties of porous media from pore-chart analysis of serial section data. J Colloid Interface Sci 1992; 150(1): 61-80. DOI: 10.1016/0021-9797(92)90268-Q.
  10. Zermatten E, Schneebeli M, Arakawa H, Steinfeld A. Tomography-based determination of porosity, specific area and permeability of snow and comparison with measurements. Cold Reg Sci Technol 2014; 97: 33-40. DOI: 10.1016/j.coldregions.2013.09.013.
  11. Shah SM, Gray F, Crawshaw JP, Boek ES. Micro-computed tomography pore-scale study of flow in porous media: Effect of voxel resolution. Adv Water Resour 2016; 95: 276-287. DOI: 10.1016/j.advwatres.2015.07.012.
  12. Miller K, Vanorio T, Keehm Y. Evolution of permeability and microstructure of tight carbonates due to numerical simulation of calcite dissolution. J Geophys Res Solid Earth 2017; 122(6): 4460-4474. DOI: 10.1002/2017JB013972.
  13. Iassonov P, Gebrenegus Th, Tuller M. Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures. Water Resour Res 2009; 45(9): 1-12. DOI: 10.1029/2009WR008087.
  14. Chauhan S, Rühaak W, Anbergen H, Kabdenov A, Freise M, Wille T, Sass I. Phase segmentation of X-ray computer tomography rock images using machine learning techniques: An accuracy and performance study. Solid Earth 2016; 7(4): 1125-1139. DOI: 10.5194/se-7-1125-2016.
  15. Bezmaternykh PV, Ilin DA, Nikolaev DP. U-Net-bin: hacking the document image binarization contest. Computer Optics 2019; 43(5): 826-833. DOI: 10.18287/2412-6179-2019-43-5-826-833.
  16. Usanov MS, Kulberg NS, Morozov SP. Experience of application of adaptive homophobic filters for computer tomograms processing. Journal of Information Technologies and Computing Systems 2017; 2: 33-42.
  17. Müter D, Pederse S, Sørensen HO, Feidenhans R, Stipp SLS. Improved segmentation of X-ray tomography data from porous rocks using a dual filtering approach. Comput and Geosci 2012; 49: 131-139. DOI: 10.1016/j.cageo.2012.06.024.
  18. Porter ML, Wildenschild D. Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study. Comput Geosci 2010; 14(1): 15-30. DOI: 10.1007/s10596-009-9130-5.
  19. Kulkarni R, Tuller M, Fink W, Wildenschild D. Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework. Vadose Zone J 2012; 11(1): 74-85. DOI: 10.2136/vzj2011.0082.
  20. Artyukov IA, Irtuganov NN. Noise-driven anisotropic diffusion filtering for X-Ray low contrast imaging. J Russ Laser Res 2019; 40(2): 150-154. DOI: 10.1007/s10946-019-09782-8.
  21. Sheppard AP, Sok RM, Averdunk H. Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A 2004; 339(1-2): 145-151. DOI: 10.1016/j.physa.2004.03.057.
  22. Van Eyndhoven G, Kurttepeli M, Van Oers CJ, Cool P, Bals S, Batenburg KJ, Sijbers J. Pore reconstruction and segmentation (PORES) method for improved porosity quantification of nanoporous materials. Ultramicroscopy 2015; 148: 10-19. DOI: 10.1016/j.ultramic.2014.08.008.
  23. Tuller M, Ramaprasad K, Fink W. Segmentation of X-ray CT data of porous materials: A review of global and locally adaptive algorithms. In Book: Anderson SH, Hopmans JW, eds. Soil–water–root processes: Advances in tomography and imaging. Ch 8. Madison, WI: Soil Science Society of America Inc; 2013: 157-182. DOI: 10.2136/sssaspecpub61.c8.
  24. Ushizima D, Morozov D, Weber GH, Bianchi AGC, Sethian JA,Wes Bethel E. Augmented topological descriptors of pore networks for material science. IEEE Trans Vis Comput Graph 2012; 18(12): 2041-2050. DOI: 10.1109/TVCG.2012.200.
  25. Wu YS, van Vliet LJ., Frijlink HW, Stokroos I, van der Voort Maarschalk K. Pore direction in relation to anisotropy of mechanical strength in a cubic starch compact. AAPS PharmSciTech 2008; 9(2): 528-535. DOI: 10.1208/s12249-008-9074-4.
  26. Zambrano M, Tondi E, Mancini L, Arzillib F, Lanzafame G, Materazzi M, Torrieri S. 3D Pore-network quantitative analysis in deformed carbonate grainstones. Mar Pet Geol 2017; 82: 251-264. DOI: 10.1016/j.marpetgeo.2017.02.001.
  27. Van De Walle W, Janssen H. Validation of a 3D pore scale prediction model for the thermal conductivity of porous building materials. Energy Procedia 2017; 132: 225-230. DOI: 10.1016/j.egypro.2017.09.759.
  28. Usamentiaga R, Garcia DF. Enhanced temperature monitoring system for sinter in a rotatory cooler. IEEE Trans Ind Appl 2016; 53(2): 1589-1597. DOI: 10.1109/TIA.2016.2626243.
  29. Grigoriev MV, Dyachkovskaya IG, Buzmakov AV, Povolotskiy MA, Kokhan VV, Chukalina MV, Uvarov VI. µCT analysis of porous cermet membranes with the use of enhanced filtration and binarization algorithms. Journal of Surface Investigation: X-Ray, Synchrotron and Neutron Techniques 2020; 14(6): 1293-1302.
  30. Ulku I, Barmpoutis P, Stathaki T, Akagunduz E. Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images. Proc SPIE 2020; 11433: 1143319. DOI: 10.1117/12.2556374.
  31. Martinez-Perez ME, Parker KH, Witt N, Hughes AD, Thom SA. Automatic artery/vein classification in colour retinal images. Proc SPIE 2020; 11433: 114331A. DOI: 10.1117/12.2557519.
  32. Al-Raoush R, Papadopoulos A. Representative elementary volume analysis of porous media using X-ray computed tomography. Powder Technol 2010; 200(1-2): 69-77. DOI: 10.1016/j.powtec.2010.02.011.
  33. Flin F, Lesaffre B, Dufour A, Gillibert L, Hasan A, du Roscoat SR, Cabanes S, Pugliese P. On the computations of specific surface area and specific grain contact area from snow 3D images. 12th International Conference on the Physics and Chemistry of Ice 2011: 321-328.
  34. Grigoriev M, Khafizov A, Kokhan V, Asadchikov V. Robust technique for representative volume element identification in noisy microtomography images of porous materials based on pores morphology and their spatial distribution. The 13th International Conference on Machine Vision (ICMV 2020). Source: <http://arxiv.org/abs/2007.03035>.
  35. Uvarov VI, Loryan VS, Borovinskaya IP, Shustov VS, Fedotov AS, Antonov DO, Tsodikov MV. Formation of the catalytically-active metalceramic membrane for the hybrid reactor [In Russian]. Novye Ogneupory 2018; 4: 87-91. DOI: 10.17073/1683-4518-2018-4-133-135.
  36. Gostick J, Khan ZA, Tranter TG, Kok MD, Agnaou M, Sadeghi MA, Jervis R. PoreSpy: A Python toolkit for quantitative analysis of porous media images. J Open Source Softw 2019; 4(37): 1296. DOI: 10.5281/zenodo.2633284.
  37. Gostick J, Aghighi M, Hinebaugh J, Tranter T, Hoeh MA, Day H, Spellacy B, Sharqawy MH, Bazylak A, Burns A, Lehnert W, Putz A. OpenPNM: a pore network modeling package. Comput Sci Eng 2016; 18(4): 60-74. DOI: 10.1109/MCSE.2016.49.
  38. Keilegavlen E, Fumagalli A, Berge R, Stefansson I, Berre I. PorePy: An open-source simulation tool for flow and transport in deformable fractured rocks. arXiv preprint 2017. Source: <https://arxiv.org/abs/1712.00460>.
  39. Li C, Wang C, Zhang S, Qiu S, Qin H. Pore-scale flow simulation in anisotropic porous material via fluid-structure coupling. Graph Models 2018; 95: 14-26. DOI: 10.1016/j.gmod.2017.12.001.
  40. Calonne N, Geindreau C, Flin F, Morin S, Lesaffre B, Du Roscoat SR, Charrier P. 3-D image-based numerical computations of snow permeability: Links to specific surface area, density, and microstructural anisotropy. Cryosphere 2012; 6(5): 939-951. DOI: 10.5194/tc-6-939-2012.
  41. Buzug TM. Computed tomography: from photon statistics to modern cone-beam CT. Berlin: Springer; 2008. DOI: 10.1007/978-3-540-39408-2.
  42. Janesick JR. Scientific charge-coupled devices. Washington: SPIE Press; 2001. DOI: 10.1117/3.374903.
  43. Kurita T, Otsu N, Abdelmalek N. Maximum likelihood thresholding based on population mixture models. Patt Recogn 1992; 25(10): 1231-1240.
  44. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990; 12(7): 629-639. DOI: 10.1109/34.56205.
  45. Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Sixth Intl Conf on Comp Vis 1998: 839-846. DOI: 10.1109/ICCV.1998.710815.
  46. Kokhan VV, Grigoriev MV, Buzmakov AV, Uvarov VI, Ingacheva AS, Chukalina MV. Correction techniques of CT-images of porous structures to increase of binarization process quality [In Russian]. Sensory Systems 2020; 34(2): 147-155. DOI: 10.31857/S0235009220020067.
  47. Iskhakov AR, Malikov RF. Calculation of aircraft area on satellite images by genetic algorithm. South Ural State University Bulletin, Series “Mathematical modelling, programming & computer software” 2016; 9(4): 148-154. DOI: 10.14529/mmp160414.
  48. Carnevali P, Coletti L, Patarnello S. Image processing by simulated annealing. Readings in Computer Vision 1987: 551-561. DOI: 10.1016/B978-0-08-051581-6.50055-6.
  49. Gonzalez R, Woods R. Digital image processing. Upper Saddle River, NJ: Prentice Hall; 2002. ISBN: 978-0-13-168728-8.
  50. Voci F, Eiho S, Sugimoto N, Sekiguchi H. Estimating the gradient threshold in the perona-malik equation. IEEE Signal Process Mag 2004; 23(3): 39-46. DOI: 10.1109/msp.2004.1296541.
  51. Chao S-M, Tsai D-M. Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images. Patt Recogn 2010; 43(5): 1917-1931. DOI: 10.1016/j.patcog.2009.12.005.
  52. Tsiotsios C, Petrou M. On the choice of the parameters for anisotropic diffusion in image processing. Patt Recogn 2013; 46(5): 1369-1381. DOI: 10.1016/j.patcog.2012.11.012.
  53. Ilyevsky A, Turkel E. Stopping criteria for anisotropic PDEs in image processing. J Sci Comput 2010; 45(1-3): 333-347. DOI: 10.1007/s10915-010-9361-6.
  54. Gilboa G. Nonlinear scale space with spatially varying stopping time. IEEE Trans Pattern Anal Mach Intell 2008; 30(12): 2175-2187. DOI: 10.1109/tpami.2008.23.
  55. Ingacheva AS, Chukalina MV. Polychromatic CT data improvement with one-parameter power correction. Math Probl Eng 2019; 2019: 1405365. DOI: 10.1155/2019/1405365.
  56. Buzmakov AV, Asadchikov VE, Zolotov DA, Chukalina MV, Ingacheva AS, Krivonosov US. Laboratory X-ray micro-CT scanners: preprocessing methods of experimental data. Bulletin of the Russian Academy of Sciences: Physics 2019; 83(2): 194-197. DOI: 10.1134/S0367676519020066.

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