Classification and numbering of teeth in dental bitewing images

1. Classification and numbering of teeth in dental bitewing images M. H. Mahoor and M. Abdel-Mottaleb Pattern Recognition, Vol. 38, No. 4, pp. 577-586, April 2005. Speaker: Cheng-Hsiung Li Date: 2005-06-02

2. Outline • Introduction • Method • Feature extraction and pre-classification • Final classification and numbering • Experiments and results • Conclusion

3. Segmentation Feature extractionand search Introduction - ADIS • An automated dental identification system Bitewing DB Identification Somebody of death Missing people

4. Feature extraction (FDs) and Bayesian classification of molars and premolars Segmentation Final classification and numbering Introduction - Motivation • The authors limit the comparison of the teeth to the ones that have the same number. • Decrease the search space • Increase the robustness of the system

5. molars premolars Method – Adult dentition system • The adult dentition contains 32 teeth, 16 teeth in each jaw.

6. Method – teeth segmentation First method -Segmentation Second method -Segmentation Segmentation Classification Feature extraction

7. jy(n) X Fourier coefficients: Feature extraction and pre-classification(1) • Complex coordinates signature • Fourier descriptors (FDs) are one of the most popular techniques for shape analysis and description. • The contour of the teeth as a complex signal u(n) defined based on the coordinates, x(n) and y(n). u(n) = x(n) + jy(n), n = 0,1,…,N-1 Fourier transform to above complex signal Segmentation Classification Feature extraction

8. Fourier coefficients: Feature extraction and pre-classification(2) • Centroid distance • The centroid distance function is expressed by the distance of the boundary points from the centroid (xc, yc) of the shape. (xc, yc) Segmentation Classification Feature extraction

9. Say c2 Say c1 P(x|ci) P(x|c1) P(x|c2) Bayesian classification of teeth • ci denote tooth class i, i.e., molar(c1) or premolar(c2) • x denote the feature vector • complex coordinates signature or centroid distance • Suppose we know the prior probability p(ci) and the conditional densities p(x|ci). • Posteriori probability Segmentation Classification Feature extraction

10. (b) (a) Arrangement of teeth in dental bitewing images. (a) left quadrant (b) right quadrant. Final classification and numbering (c) (d) Classification and numbering of the teeth in dental bitewing images. (c) left quadrant (d) right quadrant

11. Experiments and results(1) • Training set • The authors used 25 bitewing images as a training set to estimate the prior distribution p(ci) and the conditional distribution p(x|ci). • Testing set • For classification, 50 images, containing 220 molar and 180 premolar.

12. Experiments and results-(2) Pre-classification of teeth using first method of segmentation Pre-classification of teeth using second method of segmentation

13. Experiments and results-(3) Final classification of teeth using first method of segmentation Final classification of teeth using second method of segmentation

14. Experiments and results-(4) Missing teeth Missclassification teeth

15. Conclusion • The authors introduced a method for robust classification and numbering of molar and premolar teeth in bitewing images using Bayesian classification.

16. Distinguish between method 1 and method 2 (a) (b) (c) (f) (e) (d) (a) Original image; (b) Result of enhancement; (c) Result of adaptive threshold; (d) Result of segmented teeth using morphological operation; (e) Bones image; (f) Final result of separated roots and crowns. Source: Automatic Human Identification based on Dental X-Ray Images

17. Fourier coefficients: … … Original image (S = 64) P = 2 P = 62 P = 64 Fourier coefficients Fourier transform (DFT) Fourier transform (DFT)

18. (c) (a) (b) . d/4 d d/4 d d d/8 d/8 Morphological image processing • Dilation (a) Set A. (b) Square structuring element (dot is the center). (c) Dilation of A by B.