Applications of Image Processing Technique in Porous Material Characterization

Nondestructive testing (NDT) provides safe operation to engineering components – it eliminates the risk of damage during operation, and does not require specific sample preparation. It has been widely used to detect and evaluate defects, or measure properties of different types of materials and engineering structures. Examples of NDT techniques include ultrasonic, radiography, infrared thermography, electromagnetic techniques and visible optical methods. The imaging principles and imaging facilities used by these techniques can be very different, but almost all the techniques listed above require image processing to some extent. In this chapter, instead of extensively exploring all the NDT techniques and corresponding image processing methods, we will focus on optical measurement technique and related image processing methods, and use porous materials as specimens.

[1]  Korris Fu-Lai Chung,et al.  A novel image thresholding method based on Parzen window estimate , 2008, Pattern Recognit..

[2]  R. Ehrlich,et al.  On the ability of a class of random models to portray the structural features of real, observed, porous media in relation to fluid flow , 2001 .

[3]  Azriel Rosenfeld,et al.  Segmentation and Estimation of Image Region Properties through Cooperative Hierarchial Computation , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Daniel T. Cobra,et al.  Image histogram modification based on a new model of the visual system nonlinearity , 1998, J. Electronic Imaging.

[5]  Olivier Monga.  Defining and computing stable representations of volume shapes from discrete trace using volume primitives: Application to 3D image analysis in soil science , 2007, Image Vis. Comput..

[6]  Hong Liu,et al.  Generalized image contrast enhancement technique based on the Heinemann contrast discrimination model , 1996, J. Electronic Imaging.

[7]  Joaquim Salvi,et al.  Review of CMOS image sensors , 2006, Microelectron. J..

[8]  G. Madelin,et al.  Imaging of multiphase fluid saturation within a porous material via sodium NMR. , 2010, Journal of magnetic resonance.

[9]  Prasanta K. Panigrahi,et al.  Locally adaptive block thresholding method with continuity constraint , 2007, Pattern Recognit. Lett..

[10]  Erwan Plougonven,et al.  Optimal removal of topological artefacts in microtomographic images of porous materials , 2011 .

[11]  J. M. White,et al.  Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction , 1983, IBM J. Res. Dev..

[12]  Dimitrios Ventzas.  Advanced Image Acquisition, Processing Techniques and Applications I , 2012 .

[13]  Lingxue Kong,et al.  Calculation of effective pore diameters in porous filtration membranes with image analysis , 2008 .

[14]  J. T. Fredrich,et al.  3D imaging of porous media using laser scanning confocal microscopy with application to microscale transport processes , 1999 .

[15]  S. Galaup,et al.  Modelisation and circulation of fluids in geological porous systems. Images analyzing and mercury porosimetry , 2007 .

[16]  Richard Szeliski,et al.  Construction of Panoramic Image Mosaics with Global and Local Alignment , 2001 .

[17]  E. Maire,et al.  Characterization of the morphology of cellular ceramics by 3D image processing of X-ray tomography , 2007 .

[18]  L. Rudin,et al.  Feature-oriented image enhancement using shock filters , 1990 .

[19]  Eric R. Fossum,et al.  CMOS image sensors: electronic camera on a chip , 1995, Proceedings of International Electron Devices Meeting.

[20]  Manabu Takahashi,et al.  In situ visualization of fluid flow image within deformed rock by X-ray CT , 2003 .

[21]  P. Callaghan,et al.  Diffraction-like effects in NMR diffusion studies of fluids in porous solids , 1991, Nature.

[22]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[23]  R. Al-Raoush,et al.  A pore-scale investigation of a multiphase porous media system. , 2005, Journal of contaminant hydrology.

[24]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[25]  K. Miyaguchi,et al.  CCD developed for scientific application by Hamamatsu , 1999 .

[26]  Jian-Hua Wang,et al.  Detection and characterization of penetrating pores in porous materials , 2007 .

[27]  G. E. Smith,et al.  Charge coupled semiconductor devices , 1970, Bell Syst. Tech. J..

[28]  Mark L. Rivers,et al.  Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates , 2011 .

[29]  V.,et al.  A Spatial Thresholding Method for Image Segmentation , 2022 .

[30]  W. Marsden.  I and J , 2012 .

[31]  Qi Tian,et al.  Algorithms for subpixel registration , 1986 .

[32]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[33]  Three-dimensional image-based modeling of lotus-type porous carbon steel and simulation of its mechanical behavior by finite element method , 2007 .

[34]  Edith Perrier,et al.  DXSoil, a library for 3D image analysis in soil science , 2002 .

[35]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  W. Brent Lindquist,et al.  Image Thresholding by Indicator Kriging , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Marsha Jo Hannah,et al.  Computer matching of areas in stereo images. , 1974 .

[38]  Karim Faez,et al.  A new wavelet-based fuzzy single and multi-channel image denoising , 2010, Image Vis. Comput..

[39]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[40]  Richard Szeliski,et al.  Video mosaics for virtual environments , 1996, IEEE Computer Graphics and Applications.

[41]  Anders Kaestner,et al.  Imaging and image processing in porous media research , 2008 .

[42]  Glenn Healey,et al.  Radiometric CCD camera calibration and noise estimation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  R. Al-Raoush,et al.  Distribution of local void ratio in porous media systems from 3D X-ray microtomography images , 2006 .

[44]  Pierre Kornprobst,et al.  Mathematical problems in image processing - partial differential equations and the calculus of variations , 2010, Applied mathematical sciences.

[45]  R. Seright,et al.  Porous structure and fluid partitioning in polyethylene cores from 3D X-ray microtomographic imaging. , 2006, Journal of colloid and interface science.

[46]  Sabine Dippel,et al.  Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform , 2002, IEEE Transactions on Medical Imaging.

[47]  P. Delmas,et al.  Image processing-based study of soil porosity and its effect on water movement through Andosol intact columns , 2009 .

[48]  Joonki Paik,et al.  Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering , 1998 .

[49]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[50]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[51]  Ralf Widenhorn,et al.  Temperature dependence of dark current in a CCD , 2002, IS&T/SPIE Electronic Imaging.

[52]  P. Philippi,et al.  3D reconstitution of porous media from image processing data using a multiscale percolation system , 2004 .

[53]  Guojun Lu,et al.  A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval , 2003, J. Vis. Commun. Image Represent..

[54]  Igor Sevostianov,et al.  Quantitative characterization of the microstructure of a porous material in the context of tortuosity , 2010 .

[55]  Richard Szeliski,et al.  Creating full view panoramic image mosaics and texture-mapped models , 1997, International Conference on Computer Graphics and Interactive Techniques.

[56]  J. Craggs.  Applied Mathematical Sciences , 1973 .

[57]  Yuchou Chang,et al.  Using collaborative learning for image contrast enhancement , 2008, 2008 19th International Conference on Pattern Recognition.

[58]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[59]  Jianping Fan,et al.  Seeded region growing: an extensive and comparative study , 2005, Pattern Recognit. Lett..

[60]  Nikolaos Michailidis,et al.  An image-based reconstruction of the 3D geometry of an Al open-cell foam and FEM modeling of the material response , 2010 .

[61]  Hans P. Moravec,et al.  The Stanford Cart and the CMU Rover , 1983, Proceedings of the IEEE.

[62]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[63]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[64]  Jeffrey J. Zarnowski,et al.  Active-pixel CMOS sensors improve their image , 1999 .

[65]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[66]  G. Moonen,et al.  Image analysis of the axonal ingrowth into poly(D,L-lactide) porous scaffolds in relation to the 3-D porous structure. , 2003, Biomaterials.

[67]  Yi Wan,et al.  Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform , 2007, IEEE Transactions on Image Processing.

[68]  Ari Visa,et al.  Fourier-Based Object Description in Defect Image Retrieval , 2006, Machine Vision and Applications.

[69]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[70]  Sergi Grau,et al.  3D reconstruction and quantification of porous structures , 2008, Comput. Graph..

[71]  B Münch,et al.  Three‐dimensional analysis of porous BaTiO3 ceramics using FIB nanotomography , 2004, Journal of microscopy.

[72]  Seth J. Teller,et al.  Spherical Mosaics with Quaternions and Dense Correlation , 2000, International Journal of Computer Vision.

[73]  P. Levitz,et al.  A new method for three-dimensional skeleton graph analysis of porous media: application to trabecular bone microarchitecture. , 2000, Journal of microscopy.

[74]  Dieter Fritsch,et al.  CCD versus CMOS - has CCD imaging come to an end? , 2001 .

[75]  Bob Svendsen,et al.  An image morphing method for 3D reconstruction and FE-analysis of pore networks in thermal spray coatings , 2010 .

[76]  Fionn Murtagh,et al.  Gray and color image contrast enhancement by the curvelet transform , 2003, IEEE Trans. Image Process..

[77]  Chun-hung Li,et al.  Minimum cross entropy thresholding , 1993, Pattern Recognit..

[78]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[79]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[80]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[81]  Dinu Coltuc,et al.  Exact histogram specification , 2006, IEEE Transactions on Image Processing.

[82]  大槻 文悟,et al.  Pore throat size and connectivity determine bone and tissue ingrowth into porous implants : three-dimensional micro-CT based structural analyses of porous bioactive titanium implants , 2007 .

[83]  Ridha Gharbi,et al.  Using Karhunen–Loéve decomposition and artificial neural network to model miscible fluid displacement in porous media , 2000 .

[84]  P. Levitz,et al.  Toolbox for 3D imaging and modeling of porous media: Relationship with transport properties , 2007 .

[85]  Michael J. Black,et al.  On the unification of line processes, outlier rejection, and robust statistics with applications in early vision , 1996, International Journal of Computer Vision.

[86]  Z. Liang,et al.  A reconstruction technique for three-dimensional porous media using image analysis and Fourier transforms , 1998 .