Presentation project Computer Vision - Teeth segmentation
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Transcript of Presentation project Computer Vision - Teeth segmentation
Project for the Computer Vision course By Anne Everars
� � 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
� Preprocessing
▶ Grayscaling ▶ Region of interest (ROI) ▶ Separation of jaws ▶ Adjust the image ▶ Separation of teeth
� � Startpoint:
� From RGB ➡ grayscale
Grayscaling
� � 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)
� Region of interest (ROI)
� � 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
� Separation of jaws
� Separation of jaws
� � Gaussian blurring ➠ more homogeneous
� Adaptive thresholding � Opening and closing morphological operations ➠ reduce noise
Adjust the image
� Gaussian blurring
� Adaptive thresholding
� Opening and closing
� = Search for maximum intensity in the y-direction
Separation of teeth
� Teeth segmentation
▶ Water shedding ▶ Mean shift filtering
� � Segment image based on similar intensity
� No proper segmentation : toothwas not sufficiently delineated ➠exterior is also flooded
Water shedding
� = (Partial) solution to the previous problem:
� Remove part of the noise (upper part of the image)
Mean shift filtering
� = (Partial) solution to the previous problem:
� Remove part of the noise (upper part of the image) � Apply a Gaussian blurring (again)
Mean shift filtering
� = (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
� = (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
� Teeth classification
▶ Hamming distance ▶ Eigenfaces ▶ Principal Component Analysis (PCA)
� � 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
� � 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
� � 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
� � Determine the principal components in both the
segmented image and the retrieved segmentation � Determine the distance between the principal
components
Principal Component Analysis (PCA)
� Questions?