Deep-biosphere consortium of fungi and prokaryotes in Eocene subseafloor basalts

Phase contrast X-ray tomography (PCT) enables the study of systems consisting of elements with similar atomic numbers. Processing datasets acquired using PCT is nontrivial because of the low-pass characteristics of the commonly used single-image phase retrieval algorithm. In this study, we introduce an image processing methodology that simultaneously utilizes both phase and attenuation components of an image obtained at a single detector distance. This novel method, combined with regularized Perona-Malik filter and bias-corrected fuzzy C-means algorithm, allows for automated segmentation of data acquired through four-dimensional PCT. Using this integrated approach, the three-dimensional coarsening morphology of an Aluminum-29.9wt% Silicon alloy can be analyzed. © 2014 Optical Society of America OCIS codes: (100.6950) Tomographic image processing; (100.2000) Digital image processing; (100.3010) Image reconstruction techniques. References and links 1. P. Cloetens, R. Barrett, J. Baruchel, J. P. Guigay, and M. Schlenker, “Phase objects in synchrotron radiation hard x-ray imaging,” J. Phys. D Appl. Phys. 29, 133–146 (1996). 2. G. Margoritondo, Elements of Synchrotron Light: for Biology, Chemistry, and Medical Research (Oxford University, 2002). 3. D. Paganin, S. C. Mayo, T. E. Gureyev, P. R. Miller, and S. W. Wilkins, “Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object,” J. Microsc. 206, 33–40 (2002). 4. X. Wu, H. Lu, and A. Yan, “Phase-contrast x-ray tomography: Contrast mechanism and roles of phase retrieval,” Eur. J. Radiol. 68S, S8–S12 (2008). 5. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE, 1988). 6. A. Burvall, U. Lundström, P. A. Takman, D. H. Larsson, and H. M. Hertz, “Phase retrieval in x-ray phase-contrast imaging suitable for tomography,” Opt. Express 19, 10359–10376 (2011). 7. D. J. Rowenhorst and P. W. Voorhees, “Measurement of interfacial evolution in three dimensions,” Ann. Rev. Mater. Res. 42, 105–124 (2012). 8. S. C. Irvine, D. M. Paganin, S. Dubsky, R. A. Lewis, and A. Fouras, “Phase retrieval for improved multidimensional velocimetric analysis of x-ray blood flow speckle patterns,” Appl. Phys. Lett. 93, 153901 (2008). 9. D. M. Paganin, T. E. Gureyev, K. M. Pavlov, R. A. Lewis, and M. Kitchen, “Phase retrieval using coherent imaging systems with linear transfer functions,” Opt. Commun. 234, 87–105 (2004). 10. T. Otaki, “Artifact halo reduction in phase contrast microscopy using apodization,” Opt. Rev. 7, 119–122 (2000). 11. O. Debeir, N. Warzee, P. V. Ham, and C. Decaestecker, “Phase contrast image segmentation by weak watershed transform assembly,” in 5th IEEE Int. Symp. Biomed. Imaging (2008), pp. 724–727. 12. M. Hammon, A. Cavallaro, M. Erdt, P. Dankerl, M. Kirshner, K. Drechsler, S. Wesarg, M. Uder, and R. Janka, “Model-based pancreas segmentation in portal venous phase contrast-enhanced ct images,” J. Digit. Imaging 26, 1082–1090 (2013). #217193 $15.00 USD Received 16 Jul 2014; revised 20 Sep 2014; accepted 21 Sep 2014; published 30 Sep 2014 (C) 2014 OSA 6 October 2014 | Vol. 22, No. 20 | DOI:10.1364/OE.22.024606 | OPTICS EXPRESS 24606 13. Y. K. Lee and W. T. Rhodes, “Nonlinear image processing by a rotating kernel transformation,” Opt. Lett. 15, 1383–1385 (1990). 14. G. Lovric, S. Barré, J. C. Schittny, M. Roth-Kleiner, M. Stampanoni, and R. Mokso, “Dose optimization approach to fast x-ray microtomography of the lung alveoli,” J. Appl. Cryst. 46, 856–860 (2013). 15. “Materials Preparation Center, Ames Laboratory, US DOE Basic Energy Sciences, Ames, IA, USA,” www.mpc.ameslab.gov. 16. E. B. Gulsoy, A. J. Shahani, J. W. Gibbs, J. L. Fife, and P. W. Voorhees, “Four-dimensional morphological evolution of coarsening of Aluminum Silicon alloy using phase-contrast x-ray tomography,” Mater. Trans., JIM 55, 161–164 (2014). 17. M. Stampanoni, A. Groso, A. Isenegger, G. Mikuljan, Q. Chen, A. Bertrand, S. Henein, R. Betemps, U. Frommherz, P. Bhler, D. Meister, M. Lange, and R. Abela, “Trends in synchrotron-based tomographic imaging: the sls experience,” Proc. SPIE 6318, 63180M (2006). 18. T. Weitkamp, D. Haas, D. Wegrzynek, and A. Rack, “ANKAphase: Software for single-distance phase retrieval from inline x-ray phase-contrast radiographs,” J. Synchrotron Rad. 18, 617–629 (2011). 19. F. Marone and M. Stampanoni, “Regridding reconstruction algorithm for real-time tomographic imaging,” J. Synchrotron Rad. 19, 1029–1037 (2012). 20. M. A. Beltran, D. M. Paganin, K. Uesugi, and M. J. Kitchen, “2d and 3d x-ray phase retrieval of multi-material objects using a single defocus distance,” Opt. Express 18, 6423–6436 (2010). 21. D. Shaked and I. Tastl, “Sharpness measure: Towards automatic image enhancement,” in IEEE Int. Conf. Im. Process. (2005), Vol. 1. 22. P. Mondregger, D. Lübbert, P. Schäfer, and R. Köhler, “Spatial resolution in bragg-magnified x-ray images as determined by fourier analysis,” Phys. Stat. Solidi A 204, 2746–2752 (2007). 23. M. L. Comer and E. J. Delp, “Segmentation of textured images using a multiresolution gaussian autoregressive model,” IEEE Trans. Image Process. 8, 408–420 (1999). 24. P. Simmons, P. Chuang, M. L. Comer, J. E. Spowart, M. D. Uchic, and M. de Graef, “Application and further development of advanced image processing algorithms for automated analysis of serial section image data,” Modelling Simul. Mater. Sci. Eng. 17, 025002 (2009). 25. F. Catté, P. L. Lions, J. M. Morel, and T. Coll, “Image selective smoothing and edge detection by nonlinear diffusion,” SIAM J. Numer. Anal. 29, 182–193 (1992). 26. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990). 27. B. Jähne and H. Haußecker, Computer Vision and Applications (Academic, 2000). 28. J. C. Russ, The Image Processing Handbook (CRC, 2010). 29. F. Voci, S. Eiho, N. Sugimoto, and H. Sekiguchi, “Estimating the gradient threshold in the perona-malik equation,” IEEE Signal Proc. Mag. 21, 39–46 (2004). 30. M. N. Ahmed, S. M. Yamany, A. A. Farag, and T. Moriarty, “Bias field estimation and adaptive segmentation of mri data using a modified fuzzy c-means algorithm,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recogn. (1999), Vol. 1. 31. J. C. Bezdek, R. Ehrlich, and W. Full, “Fcm: The fuzzy c-means clustering algorithm,” Comput. Geosci. 10, 191–203 (1984). 32. J. C. Dunn, “A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters,” J. Cybern. 3, 32–57 (1973). 33. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. SMC-9, 62–66 (1979). 34. J. A. Hartigan and P. M. Hartigan, “The dip test of unimodality,” Ann. Stat. 13, 70–83 (1985). 35. M. Bertalmı́o, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting,” in Proc. SIGGRAPH 2000 (2000). 36. M. Mainberger, A. Bruhn, J. Weickert, and S. Forchhammer, “Optimising spatial and tonal data for homogeneous diffusion inpainting,” Pattern Recognit. 44, 1859–1873 (2011). 37. MATLAB, version 7.14.0 (R2012a) (The MathWorks Inc., Natick, Massachusetts, 2012). 38. N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series (MIT, 1964). 39. L. Hubert and P. Arabie, “Comparing partitions,” J. Classif. 2, 193–218 (1985). 40. W. M. Rand, “Objective criteria for the evaluation of clustering methods,” J. Am. Stat. Assoc. 66, 846–850 (1971). 41. A. J. Shahani, E. B. Gulsoy, J. W. Gibbs, and P. W. Voorhees, “Four-dimensional morphological characterization of Al-Si alloy during coarsening” (2014), Manuscript in preparation.