Metal Artefact Reduction from Dental CBCT Image Using Morphology and Fuzzy Logic

  • Anita Thakur
  • Vishu Pargain
  • Pratul Singh
  • Shekhar Raj Chauhan
  • P. K. Khare
  • Prashant Mor
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)


Cone beam computed tomography (CBCT) is a new-generation 3D image modality which is highly used in dentistry. As CBCT is a low-radiation imaging technique, reconstruction of image is prone to artefacts. Artefacts are the discrepancies between the original physical image and the mathematical modelling image process. In dental treatment, mostly metallic filling is done which produces metal artefact in imaging, in which it produces the reflection effect on imaging that misleads the diagnosis of treatment. In this paper, the proposed method reduces the reflection effect of metal artefacts and enhances the contrast of CBCT image. Here the proposed technique used morphological approach for reflection reduction, and fuzzy enhancement is used for contrast improvement. The output image has been analysed and evaluated using structure of similarity index matrix (SSIM) and peak value ratio in terms of signal versus noise (PSNR). Visual perception also shows the performance of the proposed work.


Metal artefact Beam hardening Morphology Fuzzy contrast enhancement 



We would like to thank Department of Electronics and Communication, Amity School of Engineering & Technology, Amity University, Uttar Pradesh, for providing us resources and facilities for implementing this research.


  1. 1.
    Miracle AC, Mukherji SK. Conebeam CT of the head and neck, part 1: physical principles. Am J Neuroradiol 2009; 30: 1088–1095.Google Scholar
  2. 2.
    Miracle AC, Mukherji SK. Conebeam CT of the head and neck, part 2: clinical applications. Am J Neuroradiol 2009; 30: 1285–1292.Google Scholar
  3. 3.
    Yu L, Li H, Mueller J, Kofler JM, Liu X, Primak AN, et al. Metal artifact reduction from reformatted projections for hip prostheses in multislice helical computed tomography: techniques and initial clinical results. Invest Radiol 2009; 44: 691–696.Google Scholar
  4. 4.
    Nahmias C, Lemmens C, Faul D, Carlson E, Long M, Blodgett T, et al. Does reducing CT artifacts from dental implants influence the PET interpretation in PET/CT studies of oral cancer and head and neck cancer? J Nucl Med 2008; 49: 1047–1052.Google Scholar
  5. 5.
    Zhang Y, Zhang L, Zhu XR, Lee AK, Chambers M, Dong L. Reducing metal artefacts in cone-beam CT images by preprocessing projection data. Int J Radiat Oncol Biol Phys 2007; 67: 924–932.Google Scholar
  6. 6.
    X. Zhang and J. Wang, “Metal artifact reduction in X-ray computed tomography by constrained optimization,” Med. Phys. 38 (2) Feb. 2011.Google Scholar
  7. 7.
    G. Wang, T. Frei and M. Vannier, “Fast Iterative Algorithm for Metal artifact reduction in Xray CT,” Acad Radiol. 7 (8), August 2000.Google Scholar
  8. 8.
    Bamberg et al., “Metal artifact reduction by dual energy CT using monoenergetic extrapolation,” Eur Radiol.,10, 2010.Google Scholar
  9. 9.
    Alvarez and Macovski, “Energy-selective reconstructions in X-ray computerised tomography,” Phys. Med. Biol., 21 (5), 1976.Google Scholar
  10. 10.
    M. Bal and L. Spies, “Metal artifact reduction in CT using tissue-class modeling and adaptive filtering,” Med. Phys., vol. 33 (8), 2006.Google Scholar
  11. 11.
    W. Veldkamp et al., “Development and Validation of segmentation and interpolation for metal aritfact suppression,” Med Phys., vol 37 (2), 2010.Google Scholar
  12. 12.
    M. Iwanowski, S. Skoneczny, J.Szostakowski, “Image segmentation by advanced morphological filtering and clustering”, in:Proceedings of International Conference of The Quantitive Description of Materials Microstructure, Warsaw 16–19 April 1997 pp. 307–314.Google Scholar
  13. 13.
    BalasubramaniamJayaram, Kakarla V.V.D.L. Narayana,V. Vetrivel, “Fuzzy Inference System based Contrast Enhancement”, EUSFLAT-LFA July,2011 Aix-les-Bains, France.Google Scholar
  14. 14.
    Li Jiuxian, Sun Wei, Xia Liangzheng, “Novel fuzzy contrast enhancement algorithm”, Journal of south east university (Natural Science Edition), Vol. 34 No 15, 2004.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Anita Thakur
    • 1
  • Vishu Pargain
    • 1
  • Pratul Singh
    • 1
  • Shekhar Raj Chauhan
    • 1
  • P. K. Khare
    • 2
  • Prashant Mor
    • 2
  1. 1.Department of Electronics and Communication EngineeringAmity UniversityNoidaIndia
  2. 2.Electronic DepartmentRDVVJabalpurIndia

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