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

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

3. Introduction - ADIS An automated dental identification system Bitewing
Segmentation
Feature extraction and search
Bitewing
* Bitewing of X-ray image (X光的照片) DB
Identification
Somebody of death
Missing people

4. 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
Segmentation
Feature extraction (FDs) and Bayesian classification of molars and premolars
Final classification and numbering

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

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

7. 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). X
jy(n)
u(n) = x(n) + jy(n), n = 0,1,…,N-1
Fourier transform to above complex signal
Fourier coefficients:
Segmentation
Feature extraction
Classification

8. 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.
Segmentation
Feature extraction
Classification

9. Bayesian classification of teeth
ci denote tooth class i, i.e., molar or premolar
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
Say c2
Say c1
P(x|c1)
P(x|c2)
P(x|ci)
Segmentation
Feature extraction
Classification

10. Final classification and numbering
First step
They search for the tooth with a confidence measure less than threshold.
Second step
* When the confidence measure of the tooth is greater than threshold and their class membership are different, then we consider the tooth with low confidence measure as a misclassified tooth and assign it to the same class.
Arrangement of teeth in dental bitewing images (a) left quadrant (b) 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. Conclusion
The authors introduced a method for robust classification and numbering of molar and premolar teeth in bitewing images using Bayesian classification.

15. Distinguish between method 1 and method 2
(c)
(f)
(d)
(e)
(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.