Presentation project Computer Vision - Teeth segmentation

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Presentation project Computer Vision - Teeth segmentation

  1. 1. Project for the Computer Vision courseBy Anne Everars
  2. 2. –™ Preprocessing–  Grayscaling–  Region of interest (ROI)–  Separation of jaws–  Adjust the image–  Separation of teeth™ Teeth segmentation–  Water shedding–  Mean shift filtering™ Teeth classificationContent
  3. 3. –Preprocessing▶ Grayscaling▶ Region of interest (ROI)▶ Separation of jaws▶ Adjust the image▶ Separation of teeth
  4. 4. –™  Startpoint:™  From RGB ➡ grayscaleGrayscaling
  5. 5. –™ Image contains a lot of extra information (jaw, nose,etc.)➠ crop to a region of interest (ROI)™ Characteristics of the radiographs:–  Variation in scale and position of head and teeth islimited–  Head is always centered horizontally™ Define the ROI based on the mean of the Gaussiandistribution of the ROI of each imageRegion of interest (ROI)
  6. 6. –Region of interest (ROI)
  7. 7. –™ Teeth have a higher grey level intensity than jawsand other (soft) tissue, because of their higher tissuedensity➠ gap between jaws forms a valley in the y-axisprojection histogram™ How?–  Determine a set of points that have minimal intensity–  Use interpolation to estimate the gap valley–  Determine a split line (parallel to the x-axis)Separation of jaws
  8. 8. –Separation of jaws
  9. 9. –Separation of jaws
  10. 10. –™ Gaussian blurring➠ more homogeneous™ Adaptive thresholding™ Opening and closing morphological operations➠ reduce noiseAdjust the image
  11. 11. –Gaussian blurring
  12. 12. –Adaptive thresholding
  13. 13. –Opening and closing
  14. 14. –= Search for maximum intensity in the y-directionSeparation of teeth
  15. 15. –Teeth segmentation▶ Water shedding▶ Mean shift filtering
  16. 16. –™ Segment image based on similar intensity™ No proper segmentation : toothwas not sufficientlydelineated➠exterior is also floodedWater shedding
  17. 17. –= (Partial) solution to the previous problem:–  Remove part of the noise (upper part of the image)Mean shift filtering
  18. 18. –= (Partial) solution to the previous problem:–  Remove part of the noise (upper part of the image)–  Apply a Gaussian blurring (again)Mean shift filtering
  19. 19. –= (Partial) solution to the previous problem:–  Remove part of the noise (upper part of the image)–  Apply a Gaussian blurring (again)–  Apply mean shift filtering to smoothen the imageMean shift filtering
  20. 20. –= (Partial) solution to the previous problem:–  Remove part of the noise (upper part of the image)–  Apply a Gaussian blurring (again)–  Apply mean shift filtering to smoothen the image–  Reapply the water shedding algorithm (and adjust theimage)Mean shift filtering
  21. 21. –Teeth classification▶ Hamming distance▶ Eigenfaces▶ Principal Component Analysis (PCA)
  22. 22. –™ Both methods do not work really well™ The delineation is not good enough to perform agood classification–  Not implemented™ Possible methods to consider:–  Hamming distance–  Eigenfaces–  Principal Component Analysis (PCA)Results fromsegmentation
  23. 23. –™  Use segmented image as a mask to compare with theretrieved segmentation= compare™  Determine common scale–  E.g. Smallest box arround both teeth™  Determine number of not-matching pixelsHamming distance
  24. 24. –™ Created bitmap that contains most characteristics ofan incisor™ If a segmented tooth can be described as a weightedsum of a number of the bitmap images, it is classifiedas an incisorEigenfaces
  25. 25. –™ Determine the principal components in both thesegmented image and the retrieved segmentation™ Determine the distance between the principalcomponentsPrincipal ComponentAnalysis (PCA)
  26. 26. –Questions?

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