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Presentation project Computer Vision - Teeth segmentation
- 1. Project for the Computer Vision course By Anne Everars
- 2. – ™ Preprocessing –  Grayscaling –  Region of interest (ROI) –  Separation of jaws –  Adjust the image –  Separation of teeth ™ Teeth segmentation –  Water shedding –  Mean shift filtering ™ Teeth classification Content
- 3. – Preprocessing ▶ Grayscaling ▶ Region of interest (ROI) ▶ Separation of jaws ▶ Adjust the image ▶ Separation of teeth
- 4. – ™  Startpoint: ™  From RGB ➡ grayscale Grayscaling
- 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 is limited –  Head is always centered horizontally ™ Define the ROI based on the mean of the Gaussian distribution of the ROI of each image Region of interest (ROI)
- 6. – Region of interest (ROI)
- 7. – ™ Teeth have a higher grey level intensity than jaws and other (soft) tissue, because of their higher tissue density ➠gap between jaws forms a valley in the y-axis projection 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
- 10. – ™ Gaussian blurring ➠more homogeneous ™ Adaptive thresholding ™ Opening and closing morphological operations ➠reduce noise Adjust the image
- 14. – = Search for maximum intensity in the y-direction Separation of teeth
- 15. – Teeth segmentation ▶ Water shedding ▶ Mean shift filtering
- 16. – ™ Segment image based on similar intensity ™ No proper segmentation : toothwas not sufficiently delineated ➠exterior is also flooded Water shedding
- 17. – = (Partial) solution to the previous problem: –  Remove part of the noise (upper part of the image) Mean shift filtering
- 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. – = (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 Mean shift filtering
- 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 the image) Mean shift filtering
- 21. – Teeth classification ▶ Hamming distance ▶ Eigenfaces ▶ Principal Component Analysis (PCA)
- 22. – ™ Both methods do not work really well ™ The delineation is not good enough to perform a good classification –  Not implemented ™ Possible methods to consider: –  Hamming distance –  Eigenfaces –  Principal Component Analysis (PCA) Results from segmentation
- 23. – ™  Use segmented image as a mask to compare with the retrieved segmentation = compare ™  Determine common scale –  E.g. Smallest box arround both teeth ™  Determine number of not-matching pixels Hamming distance
- 24. – ™ Created bitmap that contains most characteristics of an incisor ™ If a segmented tooth can be described as a weighted sum of a number of the bitmap images, it is classified as an incisor Eigenfaces
- 25. – ™ Determine the principal components in both the segmented image and the retrieved segmentation ™ Determine the distance between the principal components Principal Component Analysis (PCA)