Dental X-Ray Image Segmentation and Object Detection Based on Phase Congruency

  • F. Sattar
  • F. O. Karray
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


Dental radiographs are essential in oral diagnostic procedures. This paper presents a new method for segmentation and object detection of dental radiograph images based on phase congruency. This phase congruency based approach provides local image structure and is invariant to image scaling, rotation, translation, variable lightning conditions, as well as process noise. Comparative experimental results and quantitative measures show the effectiveness of the proposed approach.


Dental Radiographs Segmentation Object Detection Phase Congruency 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Haring, J.I., Jansen, L.: Dental radiography - principles and techniques, 2nd edn. W.B. Saunders Company (2000)Google Scholar
  2. 2.
    Nomir, O., Abdel-Mottaleb, M.: A system for human identification from X-ray dental radiographs. Pattern Recognition 38, 1295–1305 (2005)CrossRefGoogle Scholar
  3. 3.
    Oprea, S., Marinescu, C., Lita, I., Jurianu, M., Visan, D.A., Cioc, I.B.: Image processing techniques used for dental x-ray image analysis. In: Proc. of IEEE International Conference on Non-destructive Testing, Picture Acquisition Methods, pp. 125–129Google Scholar
  4. 4.
    Razmus, T.F., Williamson, G.F.: Current oral and maxillofacial imaging, 1st edn. W.B. Saunders Company (1996)Google Scholar
  5. 5.
    Nomir, O., Abdel-Mottaleb, M.: Hierarchical contour matching for dental X-ray radiographs. Pattern Recognition 41, 130–138 (2008)zbMATHCrossRefGoogle Scholar
  6. 6.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  7. 7.
    Oppenheim, A., Lim, J.: The importance of phase in signals. Proceedings of the IEEE 69, 529–541 (1981)CrossRefGoogle Scholar
  8. 8.
    Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A 4, 2379–2394 (1987)CrossRefGoogle Scholar
  9. 9.
    Kovesi, P. D.: MATLAB functions for computer vision and image analysis. School of Computer Science & Software Engineering, The University of Western Australia,
  10. 10.
    Rodriguez-Sanchez, R., Garcia, J.A., Fdez-Valdivia, J., Fdez-Vidal, R.X.: How to define the notion of microcalcilcifications in digitized mammograms. In: International Conference on Pattern Recognition (ICPR 2000), vol. 1, pp. 494–499 (2000)Google Scholar
  11. 11.
    Mancas-Thillou, C., Gosselin, B.: Character segmentation-by-recognition using log-gabor filters. In: Proc. of IAPR International Conference on Pattern Recognition (ICPR 2006), Hong Kong, China (2006)Google Scholar
  12. 12.
    Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic Publishers (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • F. Sattar
    • 1
  • F. O. Karray
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

Personalised recommendations