Skip to main content

Advertisement

SpringerLink
Log in

Automatic localization of inferior alveolar nerve canal in panoramic dental images

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Dental implants and maxillofacial procedures need forecast planning to circumvent inferior alveolar nerve canal (IAC) damage. Advancement in image processing techniques aids in the automation of IAC localization in maxillofacial regions of cone beam computed tomography-reconstructed panoramic X-ray images and reduces the complexities during the procedure. The proposed technique has been implemented on dental panoramic images and it involves intensity mapping and contrast limited adaptive histogram equalization technique to enhance the image and further the enhanced image is segregated as upper and lower jaws using B-spline technique. Then, the lower jaw is divided into two portions by vertical integral projection technique. Subsequently, the edge region of tooth and canal regions has been obtained by local phase congruency system. The predominant edge points extracted in the previous step are considered as Binary Robust Invariant Scalable Key points, which in turn are considered the feature descriptors for IAC segmentation. The feature points falling within the range of coordinates are connected using curve fitting approach to distinguish the IAC. Next, the performance of the work has been evaluated and finally it is compared with the recent techniques. The proposed method provides a higher accuracy of 92% and improved dice coefficient of 0.829 ± 0.13 compared to the deep learning method. This method provides the enhanced results and it has an ability to guide the dentist for the preoperative diagnostic process to locate the IAC successfully as well as to minimize the complexities associated with oral surgery and implantology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Shin, Y., Roh, B.-D., Kim, Y., Kim, T., Kim, H.: Accidental injury of the inferior alveolar nerve due to the extrusion of calcium hydroxide in endodontic treatment: a case report. Restorat. Dent. Endod. 41(1), 63–67 (2016). https://doi.org/10.5395/rde.2016.41.1.63

    Article  Google Scholar 

  2. Martine-Lage Azorin, J.F., Andres, G.S., Molina, R.P.V., Muries, C.A., Panadero, R.A.: Prevention and treatment of IAN injuries: a literature review. JBR J. Interdiscip. Med. Dent. Sci. 2(3), 1000123 (2014). https://doi.org/10.4172/2376-032X.1000123

    Article  Google Scholar 

  3. Wessberg, G.A., Wolford, L.M., Epker, B.N.: Experiences with microsurgical reconstruction of the inferior alveolar nerce. J. Oral Maxillofac. Surg. 40, 651–655 (1982). https://doi.org/10.1016/0278-2391(82)90115-X

    Article  Google Scholar 

  4. Mahon, N., Stassen, L.F.A.: Post-extraction inferior alveolar nerve neurosensory disturbances—a guide to their evaluation and practical management. J. Ir. Dent. Assoc. 60(5), 241–250 (2014)

    Google Scholar 

  5. Na, J.Y., Han, S.-S., Jeon, K.J., Choi, Y.J., Choi, S.H., Lee, C.: Prognosis in case of nerve disturbance after mandibular implant surgery in relation to computed tomography findings and symptoms. J Periodontal Implant Sci. 49(2), 127–135 (2019). https://doi.org/10.5051/jpis.2019.49.2.127

    Article  Google Scholar 

  6. Pippi, R.: A case of inferior alveolar nerve entrapment in the roots of a partially erupted mandibular third molar. J. Oral Maxillofac. Surg 68, 1170–1173 (2010). https://doi.org/10.1016/j.joms.2009.10.007

    Article  Google Scholar 

  7. Thangavelu, K., Kannan, R., Senthil Kumar, N., Rethish, E., Sabitha, S., Sayeeganesh, N.: Significance of localization of mandibular foramen in an inferior alveolar nerve block. J. Nat. Sci. Biol. Med. 3(2), 156–160 (2016). https://doi.org/10.4103/0976-9668.101896

    Article  Google Scholar 

  8. Sotthivirat, S., Narkbuakaew, W.: Automatic detection of inferior alveolar nerve canals on CT images. Proc. IEEE Trans. Biomed. Circuits Syst. Conf. 2006, 142–145 (2006)

    Google Scholar 

  9. Yamada, T., Ishimaha, K., et al.: Inferior alveolar nerve canal and branches detected with dental cone beam computed tomography in lower third molar region. American association of oral and maxillofacial surgeons. J. Oral Maxillofac. Surg. 69, 1278–1282 (2011). https://doi.org/10.1016/j.joms.2010.07.010

    Article  Google Scholar 

  10. Kim, G., et al.: Automatic extraction of inferior alveolar nerve canal using feature-enhancing panoramic volume rendering. IEEE Trans. Biomed. Eng. 58(2), 253–264 (2011). https://doi.org/10.1109/TBME.2010.2089053

    Article  Google Scholar 

  11. Vinayahalingam, S., Xi, T., Berge, S., Maal, T., De Jong, G.: Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 9, 9007 (2019). https://doi.org/10.1038/s41598-019-45487-3

    Article  Google Scholar 

  12. Kwak, G.H., Kwak, E.J., Song, J.M., Park, H.R., Jung, Y., Cho, B., Hui, P., Hwang, J.J.: Automatic mandibular canal detection using a deep convolutional neural network. Nat. Sci. Rep. 2020, 5711 (2020). https://doi.org/10.1038/s41598-020-62586-8

    Article  Google Scholar 

  13. Hisham, M.B., Yaakob, S.N., Raof, R.A.A., Nazren, A.B.A, Wafi, N.M.: Template matching using sum of squared difference and normalized cross correlation. In: IEEE Student Conference on Research and Development, SCOReD (2015)

  14. Bar-Gera, H.: The Target Parameter of Adjusted R-Square in Fixed-Design Experiments. The American Statistician. Taylor & Francis, London (2016)

    Google Scholar 

  15. https://in.mathworks.com/help/curvefit/evaluating-goodness-of-fit.html

  16. Roy, A.S., Dhar, A.S.: A Novel Approach for Fast and Accurate Mean Error Distance Computation in Approximate Adders. arXiv:1803.08005v1 (2018)

  17. Aljanabi, M.A., Hussian, Z.M., Lu, S.F.: An Entropy-Histogram Approach for Image Similarity and Face Recognition. Mathematics Problems in Engineering, Hindawi (2018). https://doi.org/10.1155/2018/9801308

    Book  Google Scholar 

  18. Mohammadi-Kambs, M., Holz, K., Somoza, M.M., Ott, A.: Hamming Distance as a Concept in DNA Molecular Recognition, pp. 1302–1308. ACS OMEGA, American Chemical Society (2017)

    Google Scholar 

  19. Schober, P., Boer, C., Schwarte, L.A.: Corrleation Coefficients Appropriate Use and Interpretation. Anesthesia-Analgesia (2018). https://doi.org/10.1213/ANE.0000000000002864

    Article  Google Scholar 

  20. Ma, J., Fan, X., Yang, S., Zhang, X., Zhu, X.: Contrast limited adaptive histogram equalization based fusion in YIQ and HSI color spaces for underwater image enhancement. Int. J. Pattern Recognit. Artif. Intell. 32(07), 1854018 (2018). https://doi.org/10.1142/S0218001418540186

    Article  Google Scholar 

  21. Pisano, E.D., Zong, S., et al.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11(4), 193–200 (1998)

    Article  Google Scholar 

  22. Briand, T., Monasse, P.: Theory and practice of image B-spline interpolation. Image Process. Line 8, 99–141 (2018). https://doi.org/10.5201/ipol.2018.221

    Article  MathSciNet  Google Scholar 

  23. Lehmann, T.M., Gonner, C., Spitzer, K.: Addendum: B-spline interpolation in medical image processing. IEEE Trans. Med. Imaging 20(7), 660–665 (2001)

    Article  Google Scholar 

  24. Ownes, Venkates: Edge detection is a projection. Pattern Recognit. Lett. 9, 223–244 (1989). https://doi.org/10.1016/0167-8655(89)90002-0

    Article  Google Scholar 

  25. Zaafouri, A., Sayadi, M., Fnaiech, F.: Edge segmentation of satellite image using phase congruency model, world academy of science, engineering and technology. Int. J. Electron. Commun. Eng. 4(1), 1135 (2010)

    Google Scholar 

  26. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary Robust Invariant Scalable Keypoints, ICCV11 (2011)

  27. Ali, I.H., Salman, S.: A performance analysis of various feature detectors and their descriptors for panorama image stitching. Int. J. Pure Appl. Math. 119(15), 147–161 (2018)

    Google Scholar 

  28. Agaian, S.S., Lentz, K.P., Grigoryan, AM.: A new measure of image enhancement. In: IASTED International Conference on Signal Processing and Communication, Marbella, Spain, pp. 19–22 (2000)

  29. Jain, A.K., Chen, H.: Matching of dental X-ray images for human identification. Pattern Recogn. 37, 15191532 (2004). https://doi.org/10.1016/j.patcog.2003.12.016

    Article  Google Scholar 

  30. Galvez, A., Iglesias, A.: Firefly algorithm for explicit B-spline curve fitting to data points. Math. Prob. Eng. 2013, 12 (2013). https://doi.org/10.1155/2013/528215

    Article  MathSciNet  MATH  Google Scholar 

  31. Dung, V.T., Tjahjowidodo, T.: A direct method to solve optimal knots of B-spline curves: an application for non-uniform B-spline curves fitting. PLoS ONE 12(3), e0173857 (2017). https://doi.org/10.1371/journal.pone.0173857

    Article  Google Scholar 

  32. Kovesi, P.: Phase congruency: a low level image invariant. Physhol. Res. 64, 136–148 (2000). https://doi.org/10.1007/s004260000024

    Article  Google Scholar 

  33. Sarfraz, M.: Fitting curve to planar digital data. In: Proceedings Sixth International Conference on Information Visualisation, London, pp. 633–638 (2002)

  34. Liu, X., Tanaka, M., Okutomi, M.: Noise level estimation using weak textured patches of a single noisy image. In: 2012 19th IEEE International Conference on Image Processing, pp. 665–668 (2012). https://doi.org/10.1109/ICIP.2012.6466947

Download references

Acknowledgements

This research work is supported and funded by the Council of Scientific and Industrial Research (CSIR)—Human Resource Development Group (SRF File No.: 08/237(0015)/2018-EMR-I).

Author information

Authors and Affiliations

  1. Department of ECE, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    Uma Maheswari Pandyan & Banumathi Arumugam

  2. Department of Oral and Maxillofacial Surgery, CSI College of Dental Science and Research, Madurai, Tamil Nadu, India

    Ulaganathan Gurunathan

  3. Department of ECE, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India

    Shahul Hameed Kopuli Ashkar Ali

Authors
  1. Uma Maheswari Pandyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Banumathi Arumugam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ulaganathan Gurunathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shahul Hameed Kopuli Ashkar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uma Maheswari Pandyan.

Ethics declarations

Conflicts of interest

Nil.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandyan, U.M., Arumugam, B., Gurunathan, U. et al. Automatic localization of inferior alveolar nerve canal in panoramic dental images. SIViP 16, 1389–1397 (2022). https://doi.org/10.1007/s11760-021-02091-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-02091-1

Keywords

Search

Navigation