3-D Shape Reconstruction Based CT Image Enhancement

  • Manoj Diwakar
  • Pardeep Kumar


In medical science, Computed Tomography is one of the powerful tools to reconstruct 3D image with measuring stack of parallel slices, where Radon transform is useful to get image slices by set of line integrals. 3D reconstructed images are very helpful to analyze multiple abnormalities in comparison of 2D images. Only CT scanned images is not sufficient to get the exact position and surface information of patients. This paper is dealing with the problem of direct 3D-reconstruction of medical images. We examined 3D surface reconstruction over 2D-CT scanned image and also tested over the simulated image. Shape from shading (SFS) technique is useful to get the geometric information for recover features from variation of shading. In this paper, Fast Marching Method (FMM) is used to get the 3D surface over the medical images (i.e., real image and synthetic image).


Computed tomography Radon transform Fast marching method Shape from shading Eikonal equation 


  1. 1.
    J-B.Thibault, K. Sauer, C. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multi-slice helical CT,” in Journal on Med. Phys., vol. 34, pp. 4526–44, 2007.CrossRefGoogle Scholar
  2. 2.
    RenJingying and Chen Shuyue, “Research on interpolation methods for cross-sections slice image,”in Journal onComputer Measurement and Control, vol. 13, pp. 729-733, 2005.Google Scholar
  3. 3.
    W. Withayachumnankul, C. Pintavirooj, M. Sangworasilp and K. Hamamoto, “3D shape recovery based on tomography,”in Proceedings IEEE Signal Processing, August, 2002.Google Scholar
  4. 4.
    M. Defrise. “A short reader’s guide to 3D tomographic reconstruction,”in Journal onComputerized Medical Imaging and Graphics, Vol. 25, pp. 113–6, 2001.CrossRefGoogle Scholar
  5. 5.
    ArtemAmirkhanov, ChristophHeinzl, Michael Reiter, Johann Kastner, and M. Eduard Groller, “Projection-Based Metal-Artifact Reduction for Industrial 3D X-ray Computed Tomography,” in IEEE Transactions on visualization and computer graphics, vol. 17, no. 12, December 2011.Google Scholar
  6. 6.
    R.Clackdoyle and M.Defrise, “Tomographic reconstruction in the 21st century. region-of-interest reconstruction from incomplete data,” in Journal onIEEE Signal Processing, vol. 60, pp. 60–80, 2010.Google Scholar
  7. 7.
    Y. Zhou, K. Panetta, and S. Agaian, "3D CT baggage image enhancement based on order statistic decomposition," in Proceedings IEEE Inter. Conf. on, 2010, pp. 287-291.Google Scholar
  8. 8.
    SangtaeAhn, Abhijit J Chaudhari, Felix Darvas, Charles A Bouman and Richard M Leahy,” Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” in Journal on Phys. Med. Biol., Vol. 53 pp. 3921, 2008.CrossRefGoogle Scholar
  9. 9.
    Edward J. Ciaccioa, Christina A. Tennysona, GovindBhagata, b, Suzanne K. Lewisa and Peter H.R. Greena, “Use of shape-from-shading to estimate three-dimensional architecture in the small intestinal lumen of celiac and control patients,” in Journal on Computer Methods and Programs in Biomedicine, Volume 111, pp. 676–684, 2013.CrossRefGoogle Scholar
  10. 10.
    J. Sethian and A. Popovici, ”3-D traveltime computation using the fast marching method,” in Journal onGEOPHYSICS, vol. 64, pp. 516–523, 1999.CrossRefGoogle Scholar
  11. 11.
    S. Venturras and I. Flaounas," Study of Radon Transformation and Application of its Inverse to NMR", Paper for " Algorithms in Molecular Biology" Course Assoc Prof. I. Emiris, 4 July, 2005.Google Scholar
  12. 12.
    Firas Wada and Mohamed Ali Hamdi, “3D Segmentation of Intravascular Ultrasound Images: A Fast-Marching Method,” in Journal on Radiology and Diagnostic Imaging, Vol. 1, pp. 29-36, 2013.Google Scholar
  13. 13.
    Mai BabikerAdm andAbasMd Said, “3D Reconstruction using Interactive Shape from Shading,” in Journal onInternational Journal of Computer Applicationsvol. 28, pp. 20-24, August 2011.Google Scholar
  14. 14.
    A. Patel and W.A.P. Smith, “Shape-from-shading driven 3D Morphable Models for Illumination Insensitive Face Recognition”.In Proc. BMVC, 2009.Google Scholar
  15. 15.
    W. M. Sheta, M. F. Mahmoud and E. H. Atta, “Evolutionary Computation Approach for Shape from Shading,”in Journal on IJICIS, vol. 5, 2005.Google Scholar
  16. 16.
    I. Kemelmacher-Shlizerman and R. Basri, “3D Face Reconstruction from a Single Image Using a Single Reference Face Shape”In IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, 2011.CrossRefGoogle Scholar
  17. 17.
    Vikram Appia and Anthony Yezzi, “Symmetric Fast Marching Schemes for Better Numerical Isotrophy,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 2298-2304, 2013.CrossRefGoogle Scholar
  18. 18.
    M. Diwakar, M. Kumar, CT image noise reduction based on adaptive Wienerfiltering with wavelet packet thresholding, in: 2014 InternationalConference on Parallel, Distributed and Grid Computing (PDGC), IEEE, 2014,pp. 94–98.Google Scholar
  19. 19.
    M. Diwakar, M. Kumar, Edge preservation based CT image denoising usingWiener filtering and thresholding in wavelet domain, in: 2016 FourthInternational Conference on Parallel, Distributed and Grid Computing(PDGC), IEEE, 2016, pp. 332–336.Google Scholar
  20. 20.
    M. Diwakar, M. Kumar, A hybrid method based CT image denoising usingnonsubsampled contourlet and curvelet transforms, in: Proceedings ofInternational Conference on Computer Vision and Image Processing,Springer, 2017, pp. 571–580.Google Scholar
  21. 21.
    M. Diwakar, M. Kumar, et al., CT image denoising based on complex wavelettransform using local adaptive thresholding and bilateral filtering., in:Proceedings of the Third International Symposium on Women in Computingand Informatics, ACM, 2015, pp. 297–302.Google Scholar
  22. 22.
    M. Kumar, M. Diwakar, CT image denoising using locally adaptive shrinkagerule in tetrolet domain, J. King Saud Univ. Comput. Inf. Sci. (2016).Google Scholar
  23. 23.
    M. Kumar, M. Diwakar, Edge preservation based CT image denoising usingwavelet and curvelet transforms, in: Proceedings of Fifth InternationalConference on Soft Computing for Problem Solving, Springer, 2016, pp.771–782.Google Scholar
  24. 24.
    M. Kumar, M. Diwakar, A new exponentially directional weighted functionbased CT image denoising using total variation, J. King Saud Univ. Comput.Inf. Sci. (2016).Google Scholar
  25. 25.
    M. Kumar, M. Diwakar, A new locally adaptive patch variation based CT image denoising, Int. J. Image Graph. Signal Process. 8 (1) (2016) 43.CrossRefGoogle Scholar
  26. 26.
    M. Diwakar, M. Kumar, A review on CT image noise and its denoising, Biomedical Signal Processing and Control, Elsvier, 2018, 42(1) pp. 73–88.CrossRefGoogle Scholar
  27. 27.
    M. Diwakar, M. Kumar, CT image denoising using NLM and correlation based wavelet packet thresholding." IET Image Processing, , 2018, 12(5) pp. 708 - 715.Google Scholar
  28. 28.
    S. Sahu, A.K. Singh, S.P. Ghrera, and M. Elhoseny, “An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE” Optics & Laser Technology, 2018.Google Scholar
  29. 29.
    Sahu, Sima, Harsh Vikram Singh, Basant Kumar, and Amit Kumar Singh. "Statistical modeling and Gaussianization procedure based de-speckling algorithm for retinal OCT images." Journal of Ambient Intelligence and Humanized Computing (2018): 1-14.Google Scholar
  30. 30.
    Sahu, Sima, Harsh Vikram Singh, Basant Kumar, and Amit Kumar Singh. "De-noising of ultrasound image using Bayesian approached heavy-tailed Cauchy distribution." Multimedia Tools and Applications (2017): 1-18.Google Scholar
  31. 31.
    Sahu, Sima, Harsh Vikram Singh, Basant Kumar, and Amit Kumar Singh. "A Bayesian multiresolution approach for noise removal in medical magnetic resonance images." Journal of Intelligent Systems (2018).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Manoj Diwakar
    • 1
  • Pardeep Kumar
    • 2
  1. 1.Department of CSEDIT UniversityDehradunIndia
  2. 2.Department of CSE and ITJaypee University Of Information TechnologySolanIndia

Personalised recommendations