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# 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
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?