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Utilizing V-Detector for Malocclusion Diagnosis in Dental Screening

Explore the application of V-detector technology in diagnosing malocclusion, utilizing X-ray images to classify different malocclusion types such as I, II, and III. Extracting features through brightness distribution and binarization with multiple thresholds to enhance accuracy. Experimentation involves comparison with SVM and the need for more normal data for training.

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Utilizing V-Detector for Malocclusion Diagnosis in Dental Screening

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  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 • Angle’s 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)/nT , • ..., • TnT−1 = Vmax − (nT − 1)(Vmax − Vmin)/nT , Binarized at the highest threshold

  10. Extract four featuresat 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.

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