Signal, Image and Video Processing

, Volume 9, Issue 1, pp 243–250 | Cite as

The use of combined illumination in segmentation of orthodontic bodies

  • Mohammadreza Yadollahi
  • Aleš Procházka
  • Magdaléna Kašparová
  • Oldřich Vyšata
Original Paper
First Online:
Received:
Revised:
Accepted:

Abstract

This paper presents new methods of orthodontic body segmentation using digital records of their plaster cast models under different types of illumination. Selected light conditions are used for the data acquisition to provide more clearly defined contours of the image components. The preliminary stage of the data processing uses the circular Hough transform, digital de-noising, and a separation of the orthodontic objects from their backgrounds employing Otsu’s thresholding method. The region-growing method using multiple seed points in a convex hull is then applied. The proposed general method identifies the common boundary of two neighboring and overlapping orthodontic objects with results enabling the efficient segmentation of digital data and their analysis through the computer network.

Keywords

Image segmentation Region-growing method Hough transform Orthodontics 
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Notes

Acknowledgments

This research has been supported by the research grant of the Institute of Chemical Technology, Prague No. MSM 6046137306.

References

  1. 1.
    Adams, R., Bischof, L.: Seeded region growing. Int. J. Pattern Recogn. Lett. 16(6), 641–647 (1994)Google Scholar
  2. 2.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111–122 (1981)CrossRefMATHGoogle Scholar
  3. 3.
    Bankman, I.N.: Handbook of Medical Image Processing and Analysis, 2nd edn. Elsevier, Amsterdam (2009)Google Scholar
  4. 4.
    Bhalerao, A.H.: Multiresolution Image Segmentation. Ph.D. Thesis, The University of Warwick (1991)Google Scholar
  5. 5.
    Burger, W., Burge, M.J.: Digital Image Processing. Springer, Berlin (2008)CrossRefGoogle Scholar
  6. 6.
    Chang, Y.B., Xia, J.J., Gateno, J., Xiong, Z., Zhou, X., Wong, S.T.C.: An automatic and robust algorithm of reestablishment of digital dental occlusion. IEEE Trans. Med. Imaging 29(9), 1652–1663 (2010)CrossRefGoogle Scholar
  7. 7.
    Chapuis, J., Schramm, A., Pappas, I., Hallermann, W., Schwenzer-Zimmerer, K., Langlotz, F., Caversaccio, M.: A new system for computer-aided preoperative planning and intraoperative navigation during corrective jaw surgery. IEEE Trans. Inf. Technol. Biomed. 11(3), 274–287 (2007)CrossRefGoogle Scholar
  8. 8.
    Chen, H., Gururajan, R.: Otsu’s threshold selection method applied in de-noising heart sound of the digital stethoscope record. Adv. Inf. Technol. Ind. Appl. 136, 239–244 (2012)Google Scholar
  9. 9.
    Coltuc, D., Becker, J.M.: Scan-directional architectures. Vector Parallel Process. VECPAR96 1215, 35–48 (1997)CrossRefGoogle Scholar
  10. 10.
    Dass, R., Priyanka, Devi, S.: Image segmentation techniques. Int. J. Electron. Commun. Technol. 3(1), 66–70 (2012)Google Scholar
  11. 11.
    Dostálová, T., Racek, J., Tauferová, E., Seydlová, M., Smutný, V., Bartoňová, M.: Composite Veneers, Crowns, and Inlay Bridges after Orthodontic Therapy - A Three-Year Prospective Study. Methods Inf. Med. 45, 191–194 (2006)Google Scholar
  12. 12.
    Fernandez, V.J., Navarrete, D.M., Arizmendi, C.V., Paxtian, Z.H., Rodriguez, J.R.: Digital circuit architecture for a median filter of grayscale images based on sorting network. NAUN Int. J. Circuits Syst. Signal Process. 5(12), 297–304 (2011)Google Scholar
  13. 13.
    Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee. IOP J. Phys. Med. Biol. 52(6), 1617 (2007)CrossRefGoogle Scholar
  14. 14.
    Gonzales, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Prentice Hall, Englewood Cliffs (2004)Google Scholar
  15. 15.
    González, V.H., Rodríguez, M.A.: Some geometrical results about the convex deficiency of a compact set. Int. J. Appl. Math. Sci. 2(15), 719–723 (2008)MATHGoogle Scholar
  16. 16.
    Hafizah, W.M., Soh, J.Z.E., Supriyanto, E., Nooh, S.M.: Automatic classification of muscle condition based on ultrasound image morphological differences. Int. J. Biol. Biomed. Eng. 6(1), 87–96 (2012)Google Scholar
  17. 17.
    Harrell, W.E., Hatcher, D.C., Bolt, R.L.: In search of anatomic truth: 3-dimensional digital modeling and the future of orthodontics. Am. J. Orthod. Dentofac. Orthop. 122(3), 325–330 (2002)CrossRefGoogle Scholar
  18. 18.
    Hart, P.E.: How the Hough transform was invented. IEEE Signal Process. Mag 26(6), 18–22 (2009)CrossRefGoogle Scholar
  19. 19.
    Hoad, C.L., Martel, A.L.: Segmentation of MR images for computer-assisted surgery of the lumbar spine. IOP J. Phys. Med. Biol. 47(19), 3503 (2002)CrossRefGoogle Scholar
  20. 20.
    Hough P.V.C.: Methods and Means for Recognizing Complex Patterns. U.S. Patent 3 069 654, Dec. 18 (1962)Google Scholar
  21. 21.
    Jain, D.N., Jain, N.: Coin recognition using circular Hough transform. Int. J. Electron. Commun. Comput. Technol. 2(3), 1 (2012)Google Scholar
  22. 22.
    Jain, L., Mahor, D.: Application of Hough transform for finding parametric curves. Int. J. Comput. Appl. Eng. Sci. 1(2), 100–103 (2011)Google Scholar
  23. 23.
    Keating, A.P., Knox, J., Bibb, R., Zhurov, A.I.: A comparison of plaster, digital and reconstructed study model accuracy. J. Orthod. 35(3), 191–201 (2008) Google Scholar
  24. 24.
    Nagabhushana, S.: Computer Vision and Image Processing, 2nd edn. New Age International, San Diego (2005)Google Scholar
  25. 25.
    Nitasha, B., Sharma, S., Sharma, R.: Comparison between circular Hough transform and modified canny edge detection algorithm for circle detection. Int. J. Eng. Res. Technol. 1(3), 15 (2012)Google Scholar
  26. 26.
    Ogodescu, A.S., Sinescu, C., Ogodescu, E.A., Negrutiu, M., Bratu, E.: Digital tools in the interdisciplinary orthodontic treatment of adult patients. NAUN Int. J. Biol. Biomed. Eng. 4(3), 97–105 (2010)Google Scholar
  27. 27.
    Petrou, M., Petrou, C.: Image Processing: The Fundamentals, 2nd edn. Wiley, Chichester (2010)CrossRefGoogle Scholar
  28. 28.
    Praveena, S.M., IlaVennila, D.: Optimization fusion approach for image segmentation using K-means algorithm. Int. J. Comput. Appl. 2(7), 18–25 (2010)Google Scholar
  29. 29.
    Rizon, M., et al.: Object detection using circular Hough transform. Am. J. Appl. Sci. 12(2), 1606–1609 (2007)Google Scholar
  30. 30.
    Shaneh, M., Golibagh, M.A.: Image enhancement using \(\alpha \)-trimmed mean \(\epsilon \)-Filters. World Acad. Sci. Eng. Technol. 5, 1156–1159 (2011)Google Scholar
  31. 31.
    Singh, G.D.: Digital diagnostics: three-dimensional modelling. Br. J. Oral Maxillofac. Surg. 46(1), 22–26 (2008)CrossRefGoogle Scholar
  32. 32.
    Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing, 2nd edn. California Technical, San Diego (1999)Google Scholar
  33. 33.
    Srinivasan, G.N., Shobha, G.: Segmentation techniques for target recognition. Int. J. Comput. Commun. 1(3) (2007)Google Scholar
  34. 34.
    Srinivasan, G.N., Shobha, G.: Segmentation techniques for ATDR. NAUN Int. J. Comput. 2(9), 165–171 (2008)Google Scholar
  35. 35.
    Tomaka, A., Tarnawski, M., Luchowski, L., Lisniewska-Machorowska, B.: Digital Dental Models and 3D Patient Photographs Registration for Orthodontic Documentation and Diagnostic Purposes. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems 2, Advances in Soft Computing, vol. 45, pp. 645–652. Springer, Berlin (2007)Google Scholar
  36. 36.
    Wider, M., Myint, Y.M., Supriyanto, E.: Comparison of histogram thresholding methods for ultrasound appendix image extraction. NAUN Int. J. Comput. 5(11), 542–549 (2011)Google Scholar
  37. 37.
    Yamamoto, K., Hayashi, S., Nishikawa, H., Nakamura, S., Mikami, T.: Measurements of dental cast profile and three-dimensional tooth movement during orthodontic treatment. IEEE Trans. Biomed. Eng. 38(4), 360–365 (1991)CrossRefGoogle Scholar
  38. 38.
    Yang, F., Jiang, T.: Pixon based image segmentation with Markov random fields. Int. J. Image Process. 12(12), 1552–1559 (2003)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Yang, J., Li, X.: Boundary detection using mathematical morphology. Pattern Recogn. Lett. 16(12), 1277–1286 (1995)CrossRefGoogle Scholar
  40. 40.
    Zhang, L., Dong, W., Zhang, D., Shi, G.: Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recogn. 43, 1531–1549 (2010)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Mohammadreza Yadollahi
    • 1
  • Aleš Procházka
    • 1
  • Magdaléna Kašparová
    • 2
  • Oldřich Vyšata
    • 1
  1. 1.Department of Computing and Control EngineeringInstitute of Chemical TechnologyPrague 6Czech Republic
  2. 2.Department of Paediatric Stomatology, 2nd Medical FacultyCharles UniversityPrague 5Czech Republic

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