1. Application of V-detector in dental diagnosis To be submitted to CEC 2006

2. background Malocclusion – diagnosis using X-ray V-detector – one-class classification

3. malocclusion Different types: I (normal bite), II (overbite), and III (underbite) Mild or severe (functional)

4. Lateral view skull X-ray

5. Existing diagnosis method Angles classification: angle ANB (3 in the picture) N A B

6. Feature extraction Brightness distribution instead of entity identification Binarization at multiple threshold Quantitatize each binary image with four real numbers

7. Remove artificial parts

8. Binarization using multiple thresholds

9. Choose thresholds & decide reference point T0 = Vmax, T1 = Vmax (Vmax Vmin)/n T,..., T nT1 = Vmax (n T 1)(Vmax Vmin)/n T, Binarized at the highest threshold

10. Extract four features at each threshold (a) Horizontal displacement x = xwhite x0, (b) Vertical displacement y = ywhite y0, (c) Displacement distance r = mean of distances between white pixels to (x0, y0) (d) Area mass A = total number of white pixel/width · height

11. Experiment results

12. Compare with SVM

13. Using half of normal data to train

14. summary A novel feature extraction is proposed. V-detector shows some potentials. Issue: a lot more normal data are desired.