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A Refined Global Segmentation of X-Ray CT Images for Multi-phase Geomaterials

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Abstract

X-ray computed tomography images of three-phase silica sand and glass bead specimens are analyzed and used to evaluate the segmentation performances of Otsu-, and recursion-based multilevel algorithms. A global image segmentation technique that combines iterative and recursive algorithms, namely a refined statistics-based global segmentation is proposed for segmenting multi-phase granular geomaterials. The performance of the proposed algorithm is tested by segmenting partially saturated silica sand and glass bead specimens. For the tested silica sand specimen, the refined statistics method estimated void ratio and degree of saturation were 0.67 and 39.35%. The estimates for the glass bead specimen yielded 0.64 and 43.49%, respectively. The true void ratio (0.66) and degree of saturation (37.71%) were determined with a user-controlled Image processing software package—Image-Pro. It was found that the proposed method estimated the void ratio and the degree of saturation with 1.52 and 4.35 percent errors for the silica sand and with 15.63 and 0.34 percent errors for the glass bead, respectively. The computational time of the proposed method was found to be shorter than other methods considered. Overall, it is concluded that the proposed technique performed better in segmenting three-phase granular geomaterials.

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References

  1. Liao, P.S., Chen, T.S., Chung, P.C.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17, 713–727 (2001)

    Google Scholar 

  2. Madra, A., Hajj, N.E., Benzeggagh, M.: X-ray microtomography applications for quantitative and qualitative analysis of porosity in woven glass fiber reinforced thermoplastic. Compos. Sci. Technol. 95, 50–58 (2014)

    Article  Google Scholar 

  3. Brice, C., Fennema, C.: Scene analysis using regions. Artif. Intell. 1(3), 205–226 (1970)

    Article  Google Scholar 

  4. Leedham, G., Yan, C., Takru, K., Joie, Mian, L. Comparison of some thresholding algorithms for text/background segmentation in difficult document images. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, vol. 2, pp. 859–864 (2003)

  5. Kohler, R.: A segmentation system based on thresholding. Comput. Graph. Image Process. 15(4), 319–338 (1981)

    Article  MathSciNet  Google Scholar 

  6. Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1294 (1993)

    Article  Google Scholar 

  7. Abdullah, S.L.S., Hambali, H.A., Jamil, N.: Segmentation of natural images using an improved thresholding-based technique. Procedia Eng. 41, 938–944 (2012)

    Article  Google Scholar 

  8. Kurita, T., Otsu, N., Abdelmalek, N.: Maximum likelihood thresholding based on population mixture models. Pattern Recogn. 25(10), 1231–1240 (1992)

    Article  Google Scholar 

  9. Arora, S., Acharya, J., Vermac, A., Panigrahi, P.K.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recogn. Lett. 29(2), 119–125 (2008)

    Article  Google Scholar 

  10. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)

    Article  Google Scholar 

  11. Singh, T.R., Roy, S., Singh, O.I., Sinam, T., Singh, K.M.: A new local adaptive thresholding technique in binarization. Int. J. Comput. Sci. Issues 8(2), 271–277 (2011)

    Google Scholar 

  12. Otsu, N.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  13. Catte, F., Lions, P., Morel, J., Coll, T.: Image selective and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992)

    Article  MathSciNet  Google Scholar 

  14. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intel. 12(7), 629–639 (1990)

    Article  Google Scholar 

  15. Sheppard, A.P., Sok, R.M., Averdunk, H.: Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A 339(1), 145–151 (2004)

    Article  Google Scholar 

  16. Buades, A., Coll, B., Morel, J.: Non-local algorithm for image denoising. In: Proceedings of IEEE CVPR, 2005

  17. De Chiffre, L.S., Carmignato, S., Kruthc, J.-P., Schmittd, R., Weckenmann, A.: Industrial applications of computed tomography. CIRP Ann. Manuf. Technol. 63(2), 655–677 (2014)

    Article  Google Scholar 

  18. Manahiloh, K.N., Muhunthan, B., Kayhanian, M., Gebremariam, S.Y.: X-ray computed tomography and nondestructive evaluation of clogging in porous concrete field samples. J. Mater. Civ. Eng. 24(8), 1103–1109 (2012)

    Article  Google Scholar 

  19. Manahiloh, K.N., Muhunthan, B., Likos, W.J.: Effective stress and the role of liquid fabric in unsaturated granular soils. Geomech Micro Macro I and II, 863–868 (2015)

    Google Scholar 

  20. Manahiloh, K.N., Muhunthan, B., Likos, W.J.: Microstructure-based effective stress formulation for unsaturated granular soils. Int. J. Geomech. (2016). https://doi.org/10.1061/(ASCE)GM.1943-5622.0000617

    Article  Google Scholar 

  21. Alshibli, K., Hasan, A.: Spatial variation of void ratio and shear band thickness in sand using X-ray computed tomography. Geotechnique 58(4), 249–257 (2008)

    Article  Google Scholar 

  22. Alshibli, K.A., Alramahi, B.A.: Microscopic evaluation of strain distribution in granular materials during shear. J Geotech Geoenviron Eng 132(1), 80–91 (2006)

    Article  Google Scholar 

  23. Alshibli, K.A., Sture, S., Costes, N.C., Frank, M.L., Lankton, M.R., Batiste, S.N., Swanson, R.A.: Assessment of localized deformation in sand using x-ray computed tomography. Geotech. Test. J. 23(3), 274–299 (2000)

    Article  Google Scholar 

  24. Masad, E., Somadevan, N.: Microstructural finite-element analysis of influence of localized strain distribution on asphalt mix properties. J. Eng. Mech. 128(10), 1105–1114 (2002)

    Article  Google Scholar 

  25. Chandan, C., Sivakumar, K., Masad, E., Fletcher, T.: Application of imaging techniques to geometry analysis of aggregate particles. J. Comput. Civ. Eng. 18(1), 75–82 (2004)

    Article  Google Scholar 

  26. Gebrenegus, T.: Application of X-ray computed tomography to study initiation and evolution of surface cracks in sand-bentonite mixtures. Ph.D., University of Idaho, Moscow (2009)

  27. Ghalib, A.M., Hryciw, R.D.: Soil particle size distribution by mosaic imaging and watershed analysis. J. Comput. Civ. Eng. 13(2), 80–87 (1999)

    Article  Google Scholar 

  28. Kim, H., Haas, C.T., Rauch, A.F., Browne, C.: 3D image segmentation of aggregates from laser profiling. Comput. Aided Civ. Infrastruct. Eng. 18(4), 254–263 (2003)

    Article  Google Scholar 

  29. Masad, E., Button, J.W.: Unified imaging approach for measuring aggregate angularity and texture. Comput. Aided Civ. Infrastruct. Eng. 15(4), 273–280 (2000)

    Article  Google Scholar 

  30. Masad, E., Jandhyala, V.K., Dasgupta, N., Somadevan, N., Shashidhar, N.: Characterization of air void distribution in asphalt mixes using x-ray computed tomography. J. Mater. Civ. Eng. 14(2), 122–129 (2002)

    Article  Google Scholar 

  31. Masad, E., Muhunthan, B., Crowe, C.: Numerical modelling of fluid flow in microscopic images of granular materials. Int. J. Numer. Anal. Met. 26(1), 53–74 (2002)

    Article  Google Scholar 

  32. Masad, E., Muhunthan, B., Shashidhar, N., Harman, T.: Internal structure characterization of asphalt concrete using image analysis. J. Comput. Civ. Eng. 13(2), 88–95 (1999)

    Article  Google Scholar 

  33. Razavi, M.R.: Characterization of microstructure and internal displacement field of sand using X-ray computed tomography. Ph.D., Washington State University, Pullman (2006).

  34. Wang, L.B., Frost, J.D., Lai, J.S.: Three-dimensional digital representation of granular material microstructure from x-ray tomography imaging. J. Comput. Civ. Eng. 18(1), 28–35 (2004)

    Article  Google Scholar 

  35. Zelelew, H.M., Papagiannakis, A.T.: A volumetrics thresholding algorithm for processing asphalt concrete X-ray CT images. Int. J. Pavement Eng. 12(6), 543–551 (2011)

    Article  Google Scholar 

  36. Al-Raoush, R.I., Willson, C.S.: Extraction of physically realistic pore network properties from three-dimensional synchrotron X-ray microtomography images of unconsolidated porous media systems. J. Hydrol. 300(1–4), 44–64 (2005)

    Article  Google Scholar 

  37. Al-Raoush, R.I., Willson, C.S.: A pore-scale investigation of a multi-porous media system. J. Contam. Hydrol. 77, 67–89 (2005)

    Article  Google Scholar 

  38. Carminati, A., Kaestner, A., Hassanein, R., Ippisch, O., Vontobel, P., Flühler, H.: Infiltration through series of soil aggregates: Neutron radiography and modeling. Adv. Water Resour. 30(5), 1168–1178 (2007)

    Article  Google Scholar 

  39. Culligan, K.A., Wildenschild, D., Christensen, B.S.B., Gray, W.G., Rivers, M.L.: Pore-scale characteristics of multiphase flow in porous media: a synchrotron-based CMT comparison of air-water and oil-water experiments. Adv. Water Resour. 29, 227–238 (2006)

    Article  Google Scholar 

  40. Iassonov, P., Gebrenegus, T., 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. 45(9), 1–12 (2009)

    Article  Google Scholar 

  41. Kaestner, A., Lehmann, E., Stampanoni, M.: Imaging and image processing in porous media research. Adv. Water Resour. 31(9), 1174–1187 (2008)

    Article  Google Scholar 

  42. Lehmann, P., Wyss, P., Flisch, A., Lehmann, E., Vontobel, P., Krafczyk, M., Kaestner, A., Beckmann, F., Gygi, A., Flühler, H.: Tomographical imaging and mathematical description of porous media used for the prediction of fluid distribution. Vadose Zone J. 5(1), 80–97 (2006)

    Article  Google Scholar 

  43. Wildenschild, D., Vaz, C.M.P., Rivers, M.L., Rikard, D, Christensen, B.S.B.: Using X-ray computed tomography in hydrology: systems, resolutions, and limitations. J. Hydrol. 267(3–4), 285–297 (2002)

    Article  Google Scholar 

  44. Arifin, A.Z., Asano, A.: Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recogn. Lett. 27(13), 1515–1521 (2006)

    Article  Google Scholar 

  45. Yang, Q., Kang, W.: General research on image segmentation algorithms. Int. J. Image Graph. Signal Process. 1, 1–8 (2009)

    Article  Google Scholar 

  46. Tsai, D.M.: A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recogn. Lett. 16, 653–666 (1995)

    Article  Google Scholar 

  47. Mathworks. 2015. Matlab 2015,Mathworks Inc., Natick, MA

  48. Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41(2), 233–260 (1988)

    Article  Google Scholar 

  49. Image-Pro Plus. [Computer software]. Version 5, Media Cybernetics, Silver Spring, MD (2018)

  50. Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. 8(8), 630–632 (1978)

    Article  Google Scholar 

  51. Van Geet, M., Lagrou, D., Swennen, R.: Porosity measurements of sedimentary rocks by means of microfocus X-ray computed tomography (μCT). In: Proceedings of Applications of X-Ray Computed Tomography in the Geosciences, pp. 51–60. Geological Society, Special Publications (2003)

  52. Vogel, H.-J., Tölke, J., Schulz, V.P., Krafczyk, M., Roth, K.: Comparison of a lattice-boltzmann model, a full-morphology model, and a pore network model for determining capillary pressure-saturation relationships. Vadose Zone J. 4(2), 380–388 (2005)

    Article  Google Scholar 

  53. Nunan, N., Ritz, K., Rivers, M., Feeney, D.S., Young, I.M.: Investigating microbial micro-habitat structure using X-ray computed tomography. Geoderma 133(3–4), 398–407 (2006)

    Article  Google Scholar 

  54. Jassogne, L., McNeill, A., Chittleborough, D.: 3D-visualization and analysis of macro- and meso-porosity of the upper horizons of a sodic, texture-contrast soil. Eur. J. Soil Sci. 58(3), 589–598 (2007)

    Article  Google Scholar 

  55. Schaap, M.G., Porter, M.L., Christensen, B.S.B., Wildenschild, D.: Comparison of pressure-saturation characteristics derived from computed tomography and lattice Boltzmann simulations. Water Resour. Res. 43(12), 1–12 (2007)

    Article  Google Scholar 

  56. Lee, S.S., Gantzer, C.J., Thompson, A.L., Anderson, S.H., Ketcham, R.A.: Using high-resolution computed tomography analysis to characterize soil-surface seals. Soil Sci. Soc. Am. J. 72(5), 1478–1485 (2008)

    Article  Google Scholar 

  57. Baveye, P.C., Laba, M., Otten, W., Bouckaert, L., Dello Sterpaio, P., Goswami, R.R., Grinev, D., Houston, A., Hu, Y., Liu, J., Mooney, S., Pajor, R., Sleutel, S., Tarquis, A., Wang, W., Wei, Q., Sezgin, M.: Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma 157(1–2), 51–63 (2010)

    Article  Google Scholar 

  58. Schlüter, S., Weller, U., Vogel, H.-J.: Segmentation of X-ray microtomography images of soil using gradient masks. Comput. Geosci. 36(10), 1246–1251 (2010)

    Article  Google Scholar 

  59. Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R., Tarquis, A.M., Gómez-Barba, L., Andina, D.: Identification of pore spaces in 3D CT soil images using PFCM partitional clustering. Geoderma 217–218, 90–101 (2014)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Center for Advanced Infrastructure and Transportation (CAIT) through the national University Transportation Centers (UTC) Program (Grant No. 5232). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of CAIT.

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  1. Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, 19716, USA

    Kalehiwot Nega Manahiloh, Kokeb A. Abera & Mohammad Motalleb Nejad

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  1. Kalehiwot Nega Manahiloh
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Correspondence to Kalehiwot Nega Manahiloh.

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Manahiloh, K.N., Abera, K.A. & Motalleb Nejad, M. A Refined Global Segmentation of X-Ray CT Images for Multi-phase Geomaterials. J Nondestruct Eval 37, 54 (2018). https://doi.org/10.1007/s10921-018-0508-y

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