Advertisement

From Projections to the 3D Analysis of the Regenerated Tissue

  • Francesco BrunEmail author
Chapter
Part of the Fundamental Biomedical Technologies book series (FBMT)

Abstract

Computational imaging techniques such as X-ray computed tomography (CT) rely on a significant amount of computing. The acquired tomographic projections are digitally processed to reconstruct the final images of interest. This process is generically called reconstruction, and it includes additional steps prior to or at the end of the execution of an actual reconstruction algorithm. Most of these steps aim at improving image quality, mainly in terms of artifacts compensation and noise reduction. The reconstructed images are then digitally analyzed to derive quantitative data and to support the qualitative visual interpretation. This part involves computational approaches that fall within the generic term image segmentation. Pre- and post- segmentation image processing is often required to improve the final quantification and extract reliable data from a CT dataset. This chapter presents an overview of the reconstruction and segmentation fundamentals for the 3D analysis of high-resolution X-ray CT data. Better knowledge about artifacts and reconstruction issues avoid misinterpretation of the images. Similarly, more insights about the limitations of image segmentation and quantification help commenting the reliability of the derived numerical values. A deeper understanding of these elements is therefore beneficial to optimize the whole workflow that starts from sample preparation and leads to CT-based scientific results.

Keywords

Computational imaging Reconstruction Segmentation Feature extraction Data reduction 

References

  1. 1.
    Kak AC, Slaney M (1988) Principles of computerized tomographic imaging. IEEE Press, New Brunswick. (Cited on pages 10 and 11)Google Scholar
  2. 2.
    Wei Y, Wang G, Hsieh J (2005) An intuitive discussion on the ideal ramp filter in computed tomography (I). Comput Math Appl 49(5–6):731–740CrossRefGoogle Scholar
  3. 3.
    Feldkamp LA, Davis LC, Kress JW (1984) Practical cone-beam algorithm. J Opt Soc Am 1:612–619CrossRefGoogle Scholar
  4. 4.
    Pan X, Sidky EY, Vannier M (2009) Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl 25(12):123009CrossRefGoogle Scholar
  5. 5.
    Brun F, Delogu P, Longo R, Dreossi D, Rigon L (2018) Inpainting approaches to fill in detector gaps in phase contrast computed tomography. Meas Sci Technol 29(1):014001CrossRefGoogle Scholar
  6. 6.
    Landweber L (1951) An iteration formula for Fredholm integral equations of the first kind. Am J Math 73:615–624CrossRefGoogle Scholar
  7. 7.
    Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand 49(6):409–436CrossRefGoogle Scholar
  8. 8.
    Gordon R, Bender R, Herman GT (1970) Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography. J Theor Biol 29(3):471–476 IN1-IN2,477-481CrossRefGoogle Scholar
  9. 9.
    Hounsfield GN (1973) Computerized transverse axial scanning (tomography): I. Description of system. Br J Radiol 46(552):1016–1022CrossRefGoogle Scholar
  10. 10.
    Rit S, Vila Oliva M, Brousmiche S, Labarbe R, Sarrut D, Sharp GC (2014) The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK). J Phys Conf Ser 489(1):012079CrossRefGoogle Scholar
  11. 11.
    van Aarle W, Palenstijn WJ, De Beenhouwer J, Altantzis T, Bals S, Batenburg KJ, Sijbers J (2015) The ASTRA toolbox: a platform for advanced algorithm development in electron tomography. Ultramicroscopy 157:35–47CrossRefGoogle Scholar
  12. 12.
    Biguri A, Dosanjh M, Hancock S, Soleimani M (2016) TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction. Biomed Phys Eng Express 2:055010CrossRefGoogle Scholar
  13. 13.
    Pelt DM, Batenburg KJ (2014) Improving filtered backprojection reconstruction by data-dependent filtering. IEEE Trans Image Process. 23(11), art. no. 2341971:4750–4762CrossRefGoogle Scholar
  14. 14.
    Pelt DM, De Andrade V (2017) Improved tomographic reconstruction of large-scale real-world data by filter optimization. Adv Struct Chem Imaging 2:17CrossRefGoogle Scholar
  15. 15.
    Barrett JF, Keat N (2004) Artifacts in CT: recognition and avoidance. Radiographics 24(6):1679–1691CrossRefGoogle Scholar
  16. 16.
    Van Nieuwenhove V, De Beenhouwer J, De Carlo F, Mancini L, Marone F, Sijbers J (2015) Dynamic intensity normalization using eigen flat fields in X-ray imaging. Opt Express 23(21):27975–27989CrossRefGoogle Scholar
  17. 17.
    Brun F, Kourousias G, Dreossi D, Mancini L, Tromba G (2011). A comparative evaluation of ring artifacts reduction filters for X-ray computed microtomography images. Proceedings of the 18th IEEE International Conference on Image Processing (ICIP). pp 405–408. Brussels, BelgiumGoogle Scholar
  18. 18.
    Paleo P, Mirone A (2015) Ring artifacts correction in compressed sensing tomographic reconstruction. J Synchrotron Radiat 22:1268–1278CrossRefGoogle Scholar
  19. 19.
    Brun F, Turco G, Paoletti S, Accardo A (2015) A synchrotron radiation microtomography study of wettability and swelling of nanocomposite alginate/hydroxyapatite scaffolds for bone tissue engineering. IFMBE Proc 51:288–291CrossRefGoogle Scholar
  20. 20.
    Brun F, Kourousias G, Dreossi D, Mancini L (2009) An improved method for ring artifacts removing in reconstructed tomographic images. IFMBE Proc 25(4):926–929CrossRefGoogle Scholar
  21. 21.
    Massimi L, Brun F, Fratini M, Bukreeva I, Cedola A (2018) An improved ring removal procedure for in-line x-ray phase contrast tomography. Phys Med Biol 63(4):045007CrossRefGoogle Scholar
  22. 22.
    Hu Q, Qian G, Nowinski WL (2005) Fast connected-component labelling in three-dimensional binary images based on iterative recursion. Comput Vis Image Underst 99(3):414–434CrossRefGoogle Scholar
  23. 23.
    Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  24. 24.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. Proceedings of the 6th IEEE International Conference on Computer Vision. pp 839–846. Bombay, IndiaGoogle Scholar
  25. 25.
    Soille P (2004) Morphological image analysis: principles and applications, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  26. 26.
    Brun F et al (2011) Automated quantitative characterization of alginate/hydroxyapatite bone tissue engineering scaffolds by means of micro-CT image analysis. J Mater Sci Mater Med 22(12):2617–2629CrossRefGoogle Scholar
  27. 27.
    Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recogn 19:41–47CrossRefGoogle Scholar
  28. 28.
    Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 8:630–632CrossRefGoogle Scholar
  29. 29.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRefGoogle Scholar
  30. 30.
    Tsai WH (1985) Moment-preserving thresholding: a new approach. Graph Models Image Process 19:377–393CrossRefGoogle Scholar
  31. 31.
    Pun T (1981) Entropic thresholding: a new approach. Comput Graph Image Process 16:210–239CrossRefGoogle Scholar
  32. 32.
    Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Graph Models Image Process 29:273–285CrossRefGoogle Scholar
  33. 33.
    Niblack W (1986) An introduction to digital image processing. Prentice Hall, Englewood CliffsGoogle Scholar
  34. 34.
    Brice CR, Fenema CL (1970) Scene analysis using regions. Artif Intell 1:205–226CrossRefGoogle Scholar
  35. 35.
    Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6):641–647CrossRefGoogle Scholar
  36. 36.
    Haralick RM, Kelly GL (1969) Pattern recognition with measurement space and spatial clustering for multiple images. Proc IEEE 57(4):654–665CrossRefGoogle Scholar
  37. 37.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Image Process 16:583–598Google Scholar
  38. 38.
    Batenburg KJ, Sijbers J (2011) DART: a practical reconstruction algorithm for discrete tomography. IEEE Trans Image Process 20(9):2542–2553CrossRefGoogle Scholar
  39. 39.
    Ohser J, Schladitz K (2009) 3D images of materials structures: processing and analysis. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  40. 40.
    Renghini C et al (2013) Microstructural characterization and in vitro bioactivity of porous glass-ceramic scaffolds for bone regeneration by synchrotron radiation X-ray microtomography. J Eur Ceram Soc 33(9):1553–1565CrossRefGoogle Scholar
  41. 41.
    Hildebrand T, Rüegsegger P (1997) A new method for the model independent assessment of thickness in three-dimensional images. J Microsc 185(1):67–75CrossRefGoogle Scholar
  42. 42.
    Ketcham RA, Ryan TM (2004) Quantification and visualization of anisotropy in trabecular bone. J Microsc 213(2):158–171CrossRefGoogle Scholar
  43. 43.
    Cowin SC, Laborde AJ (1985) The relationship between the elasticity tensor and the fabric tensor. Mech Mater 4(22):137–147CrossRefGoogle Scholar
  44. 44.
    Whitehouse WJ (1974) The quantitative morphology of anisotropic trabecular bone. J Microsc 101:153–168CrossRefGoogle Scholar
  45. 45.
    Harrigan TP, Mann RW (1984) Characterization of microstructural anisotropy in orthotropic materials using a second rank tensor. J Mater Sci 19(3):761–767CrossRefGoogle Scholar
  46. 46.
    Cornea ND, Silver D, Min P (2007) Curve-skeletons properties, applications and algorithms. IEEE Trans Vis Comput Graph 13(3):530–548CrossRefGoogle Scholar
  47. 47.
    Odgaard A, Gundersen HJG (1993) Quantification of connectivity in cancellous bone, with special emphasis on 3-D reconstructions. Bone 14(2):173–182CrossRefGoogle Scholar
  48. 48.
    Brun F, Dreossi D (2010) Efficient curve-skeleton computation for the analysis of biomedical 3D images. Biomed Sci Instrum 46:475–480PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Engineering and ArchitectureUniversity of TriesteTriesteItaly
  2. 2.National Institute for Nuclear Physics (INFN) – Trieste DivisionTriesteItaly

Personalised recommendations