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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
  1 FSRC "Crystallography and Photonics" RAS, 119333, Russia, Moscow, Leninskiy Prospekt, 59,
  2 Smart Engines LLC, 117312, Russia, Moscow, 60-letiya Oktyabrya Ave, 9,
  3 Institute for Information Transmission Problems RAS, 127051, Russia, Moscow, Karetny per. 19, build. 1,
  4 NRC Kurchatov Institute, 123098, Russia, Moscow, Ploshchad' Akademika Kurchatova, 1, b. 113,
  5 Institute of Structural Macrokinetics and Materials Science RAS,
                        142432, Russia, Chernogolovka, Ulitsa Akademika Osip'yana, 8,
  6 Institute of Microelectronics Technology and High-Purity Materials of the Russian Academy of Sciences,
                        142432, Russia, Chernogolovka, Ulitsa Akademika Osip'yana, 6
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DOI: 10.18287/2412-6179-CO-781
Страницы: 110-121.
Язык статьи: English
Аннотация:
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.
Ключевые слова:
postprocessing of CT-images, CT images of porous structures, representative volume element, anisotropic porous structures.
Благодарности
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).
Citation:
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.
Литература:
  - 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.
- 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. 
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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. 
 
- 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.
 
- 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. 
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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>.
 
- 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.
 
- 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.
 
- 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.
 
- 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>.
 
- 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. 
 
- 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. 
 
- Buzug TM. Computed tomography: from photon  statistics to modern cone-beam CT. Berlin: Springer;  2008. DOI: 10.1007/978-3-540-39408-2.
 
- Janesick JR. Scientific charge-coupled devices. Washington: SPIE Press;  2001. DOI: 10.1117/3.374903.
 
- Kurita T, Otsu N, Abdelmalek N.  Maximum likelihood thresholding based on population mixture models. Patt Recogn  1992; 25(10): 1231-1240. 
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- Gonzalez R, Woods R. Digital image processing. Upper Saddle River,   NJ: Prentice Hall; 2002. ISBN: 978-0-13-168728-8.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- 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.
 
- Ingacheva AS, Chukalina MV. Polychromatic CT  data improvement with one-parameter power correction. Math Probl Eng 2019; 2019:  1405365. DOI: 10.1155/2019/1405365.       
      
- 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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