Skip to main content

Advertisement

SpringerLink
Log in

Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Teeth arrangement is essential in face ergonomics and healthiness. In addition, they play key roles in forensic medicine. Various computer-assisted procedures for medical application in quantitative dentistry require automatic classification and numbering of teeth in dental images.

Method

In this paper, we propose a multi-stage technique to classify teeth in multi-slice CT (MSCT) images. The proposed algorithm consists of the following three stages: segmentation, feature extraction and classification. We segment the teeth by employing several techniques including Otsu thresholding, morphological operations, panoramic re-sampling and variational level set. In the feature extraction stage, we follow a multi-resolution approach utilizing wavelet-Fourier descriptor (WFD) together with a centroid distance signature. We compute the feature vector of each tooth by employing the slice associated with largest tooth tissues. The feature vectors are employed for classification in the third stage. We perform teeth classification by a conventional supervised classifier. We employ a feed- forward neural network classifier to discriminate different teeth from each other.

Results

The performance of the proposed method was evaluated in the presence of 30 different MSCT data sets including 804 teeth. We compare classification results of the WFD technique with Fourier descriptor (FD) and wavelet descriptor (WD) techniques. We also investigate the invariance properties of the WFD technique. Experimental results reveal the effectiveness of the proposed method.

Conclusion

We provided an integrated solution for teeth classification in multi-slice CT datasets. In this regard, suggested segmentation technique was successful to separate teeth from each other. The employed WFD approach was successful to discriminate and numbering of the teeth in the presence of missing teeth. The solution is independent of anatomical information such as knowing the sequence of teeth and the location of each tooth in the jaw.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Fuller J, Denehy G (2001) Concise dental anatomy and morphology. The University of Lowa press, Ames

    Google Scholar 

  2. Pongrdcz F, Bdrdosi Z (2006) Dentition planning with image-based occlusion analysis. Int J CARS 1: 149–156. doi:10.1007/s11548-006-0052-6

    Article  Google Scholar 

  3. Valente F, Sbrenna A, Buoni C (2006) CAD CAM drilling guides for transferring CT-based digital planning to flapless placement of oral implants in complex cases. Int J CARS 1: 413–426. doi:10.1007/s11548-006-0025-9

    Article  Google Scholar 

  4. Eggers G, Kress B, Fiebach J, Rieker M, Spitzenberg D, Ghanai S, Marmulla R, Muhling J, Dickhaus H (2006) MRI-based creation of jaw models for therapy planning. Int J CARS 1: 427–435. doi:10.1007/s11548-006-0035-7

    Article  Google Scholar 

  5. Yoo K, Ha J (2005) An effective modeling of single cores prostheses using geometric techniques. Comput Aided Des 37: 35–44

    Article  Google Scholar 

  6. Bossard D, Dubos N, Trunde F, Huet A, Coudert JL (2004) 3D computed-assisted surgery in orthodontic treatment of impacted canines in palatal position. Int Congr Ser 1268: 1203–1208

    Article  Google Scholar 

  7. Jain AK, Chen H (2004) Matching of dental X-ray images for human identification. J Pattern Recognit 37: 1519–1532

    Article  Google Scholar 

  8. Nomir O, Abdel-Mottaleb M (2005) A system for human identification from X-ray dental radiographs. J Pattern Recognit 38: 1295–1305

    Article  Google Scholar 

  9. Zhou J, Abdel-Mottaleb M (2005) A content-based system for human identification based on bitewing dental X-ray images. J Pattern Recognit 38: 2132–2142

    Article  Google Scholar 

  10. Sidler M, Jackowski C, Dirnhofer R, Vock P, Thali M (2006) Use of multislice computed tomography in disaster victim Identification-Advantages and limitations. Forensic Sci Int 38: 2132–2142

    Google Scholar 

  11. Murray D, Whyte A (2002) Dental panaromic tomography: what the general radiologist needs to know. Clin Radiol 57: 1–7

    Article  PubMed  Google Scholar 

  12. Mahoor MH, Abdel-Mottaleb M (2005) Classification and numbering of teeth in dental bitewing images. Pattern Recognit 38: 577–586

    Article  Google Scholar 

  13. Jain AK, Chen H (2005) Registration of dental atlas to radiographs for human identification. In: Proceedings of international society for optical engineering (SPIE), Conference on Biometric Technology for human Identification, vol 5779. Orlando, pp 292–298

  14. Scarfe WC, Farman AG (2006) Clnical application of cone-beam computed tomography in dental practice. J Can Dent Assoc 72: 75–80

    PubMed  Google Scholar 

  15. Kirchhoff S, Fischer F, Lindemaier G, Herzog P, Kirchhoff C, Becker C, Bark J, Reiser MF, Eisenmenger W (2008) Is post-mortem CT of the dentition adequate for correct forensic identification? comparison of dental computed. Int J Legal Med 122: 471–479

    Article  CAS  PubMed  Google Scholar 

  16. Jackowski C, Aghayev E, Sonnenschein M, Dirnhofer R, Thali MJ (2006) Maximum intensity projection of cranial computed tomography data for dental identification. Int J Legal Med 120(3): 165–167

    Article  CAS  PubMed  Google Scholar 

  17. Jackowski C, Lussi A, Classens M, Kilchoer T, Bolliger S, Aghayev E, Criste A, Dirnhofer R, Thali MJ (2006) Extended CT scale overcomes restoration caused streak artifacts for dental identification in CT-3D color encoded automatic discrimination of dental restorations. J Comput Assist Tomogr 30(3): 510–513

    Article  CAS  PubMed  Google Scholar 

  18. Thali MJ, Markwalder T, Jackowski C, Sonnenschein M, Dirnhofer R (2006) Dental CT imaging as a screening tool for dental profiling: advantages and limitations. J Forensic Sci 51(1): 113–119

    Article  PubMed  Google Scholar 

  19. Momeni M, Zoroofi RA (2008) Automated dental recognition by wavelet descriptors in CT multi-slices data. Int J CARS 3: 533–542. doi:10.1007/s11548-008-0255-0

    Article  Google Scholar 

  20. Hosntalab M, Zoroofi RA, Abbaspour Tehrani-Fard A, Shirani G (2008) Segmentation of teeth in CT volumetric data set by panoramic projection and variational level set. Proc 22nd Int Congress and Exhibition CARS, p S442. doi:10.1007/s11548-008-0208-7

  21. Hosntalab M, Zoroofi RA, Abbaspour Tehrani-Fard A, Shirani G (2008) Segmentation of teeth in CT volumetric data set by panoramic projection and variational level set. Int J CARS 3(3–4): 257–265. doi:10.1007/s11548-008-0230-9

    Article  Google Scholar 

  22. Baba R, Ueda K, Okabe M (2004) Using a flat-panel detector in high resolution cone beam CT for dental imaging. Dentomaxillofac Radiol 33(5): 285–290

    Article  CAS  PubMed  Google Scholar 

  23. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1): 62–66

    Article  Google Scholar 

  24. Akhoondali H, Zoroofi RA (2009) Rapid automatic segmentation and visualization of teeth in CT-scan data. J Appl Sci 9(11): 2031–2044. doi:10.3923/jas.2009.2031.2044

    Article  Google Scholar 

  25. Gonzales R, Woods R (2002) Digital image processing, 2nd edn. Prentice Hall, New Jersey

    Google Scholar 

  26. Zhao HK, Chan TF, Merriman B, Osher S (1996) A variational level set approach to multiphase motion. J Comput Phys 127(1): 79–195

    Article  Google Scholar 

  27. Vese L, Chan T (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vision 50(3): 271–293

    Article  Google Scholar 

  28. Strumas N, Antonyshyn O, Yaffe MJ, Mawdsley G, Cooper P (1998) Computed tomography artefacts: an experimental investigation of causative factors. Can J Plast Surg 1: 23–29

    Google Scholar 

  29. Zhang D, Lu G (2004) Review of shape representation and dscription techniques. Pattern Recognit 37: 1–19

    Article  Google Scholar 

  30. Zahn CT, Roskies RZ (1972) Fourier descriptors for plane closed curves. IEEE Trans Comput 21(3): 269–281

    Article  Google Scholar 

  31. Persoon E, Fu K-S (1977) Shape discrimination using Fourier descriptors. IEEE Trans Systems Man Cybern (SMC) 7: 170–179

    Article  Google Scholar 

  32. Chang GC, Kuo CCJ (1996) Wavelet descriptor of planer curves: theory and applications. IEEE Trans Image Process 5: 56–70

    Article  Google Scholar 

  33. Tieng QM, Boles WW (1997) Recognition of 2D object contours using the wavelet transform zero-crossing representation. IEEE Trans Pattern Anal Mach Intell 19(8): 910–916

    Article  Google Scholar 

  34. Yang HS, Lee SU, Lee KM (1998) Recognition of 2-D object contours using starting-point independent wavelet coefficient matching. J Vis Commun Image R 9: 171–181

    Article  Google Scholar 

  35. Misiti M, Misiti Y, Oppenheim G, Poggi JM (2007) Wavelets and their applications, 1st edn. ISTE Ltd, London

    Google Scholar 

  36. Chen G, Bui TD (1999) Invariant Fourier-wavelet descriptors for pattern recognition. Pattern Recognit 32: 1083–1088

    Article  Google Scholar 

  37. Yadav RB, Nishchal NK, Gupta AK, Rastogi VK (2007) Retrieval and classification of shape-based objects using Fourier, generic Fourier, and wavelet-Fourier descriptors technique: A comparative study. Opt Lasers Eng 45: 695–708

    Article  Google Scholar 

  38. Osowski S, Nghia DD (2002) Fourier and wavelet descriptors for shape recognition using neural networks-a comparative study. Pattern Recogniti 35: 1949–1957

    Article  Google Scholar 

  39. http://www.neurosolutions.com

  40. http://www.mathworks.com

  41. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8): 861–874

    Article  Google Scholar 

  42. Newman TS, Yi H (2006) A survey of the marching cubes algorithm. Comput Graph 30(5): 854–879

    Article  Google Scholar 

  43. http://www.vtk.org

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran

    Mohammad Hosntalab & Ali Abbaspour Tehrani-Fard

  2. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Reza Aghaeizadeh Zoroofi

  3. Oral and Maxillofacial Surgery Department, Faculty of Dentistry Medical Science of Tehran University, Tehran, Iran

    Gholamreza Shirani

Authors
  1. Mohammad Hosntalab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Aghaeizadeh Zoroofi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Abbaspour Tehrani-Fard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gholamreza Shirani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hosntalab.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hosntalab, M., Aghaeizadeh Zoroofi, R., Abbaspour Tehrani-Fard, A. et al. Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor. Int J CARS 5, 237–249 (2010). https://doi.org/10.1007/s11548-009-0389-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-009-0389-8

Keywords

Search

Navigation