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Classification and numbering of teeth in dental bitewing images
M. H. Mahoor and M. Abdel-MottalebPattern Recognition, Vol. 38, No. 4, pp.
577-586, April 2005.
Speaker: Cheng-Hsiung LiDate: 2005-06-02
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Outline
Introduction Method Feature extraction and pre-classification Final classification and numbering Experiments and results Conclusion
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Introduction - ADIS
An automated dental identification system
Feature extractionand searchBitewing
Segmentation
DBIdentification
Somebody of death
Missing people
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Introduction - Motivation
The authors limit the comparison of the teeth to the ones that have the same number. Decrease the search space Increase the robustness of the system
Segmentation Feature extraction (FDs) and Bayesian classification of
molars and premolars
Final classification and numbering
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Method – Adult dentition system
The adult dentition contains 32 teeth, 16 teeth in each jaw.
molars
premolars
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Method – teeth segmentation
First method -Segmentation
Second method -Segmentation
Feature extractionSegmentation Classification
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Feature extraction and pre-classification(1)
Complex coordinates signature Fourier descriptors (FDs) are one of the most popular
techniques for shape analysis and description. The contour of the teeth as a complex signal u(n) defined
based on the coordinates, x(n) and y(n).
u(n) = x(n) + jy(n), n = 0,1,…,N-1X
jy(n)
Fourier coefficients:
Fourier transform to above complex signal
Feature extractionSegmentation Classification
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Feature extraction and pre-classification(2)
Centroid distance The centroid distance function is expressed by the distance of
the boundary points from the centroid (xc, yc) of the shape.
Feature extractionSegmentation Classification
Fourier coefficients:
(xc, yc)
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Bayesian classification of teeth
ci denote tooth class i, i.e., molar(c1) or premolar(c2)
x denote the feature vector complex coordinates signature or centroid distance
Suppose we know the prior probability p(ci) and the conditional densities p(x|ci).
Posteriori probability
Feature extractionSegmentation Classification
Say c2Say c1
P(x|c1) P(x|c2)
P(x|ci)
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Final classification and numbering
Arrangement of teeth in dental bitewing images. (a) left quadrant (b) right quadrant.
(a)
(b)
Classification and numbering of the teeth in dental bitewing images. (c) left quadrant (d) right quadrant
(c)
(d)
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Experiments and results(1)
Training set The authors used 25 bitewing images as a training
set to estimate the prior distribution p(ci) and the conditional distribution p(x|ci).
Testing set For classification, 50 images, containing 220 molar
and 180 premolar.
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Experiments and results-(2)
Pre-classification of teeth using first method of segmentation
Pre-classification of teeth using second method of segmentation
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Experiments and results-(3)
Final classification of teeth using first method of segmentation
Final classification of teeth using second method of segmentation
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Experiments and results-(4)
Missing teeth
Missclassification teeth
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Conclusion
The authors introduced a method for robust classification and numbering of molar and premolar teeth in bitewing images using Bayesian classification.
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Distinguish between method 1 and method 2
(a) Original image; (b) Result of enhancement; (c) Result of adaptive threshold; (d) Result of segmented teeth using morphological operation; (e)
Bones image; (f) Final result of separated roots and crowns.
(a) (b) (c)
(d) (e) (f)
Source: Automatic Human Identification based on Dental X-Ray Images
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Fourier coefficients
Fourier coefficients:
Fourier transform (DFT)
∑−
=
=1
0
/2)()(N
s
NsnjesUnu π
Fourier transform (DFT)
… …Original image
(S = 64) P = 2 P = 62 P = 64
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Morphological image processing
Dilation
d
d
(a)
.
d/4
d/4
(b)
d
(c)
d/8 d/8
(a) Set A. (b) Square structuring element (dot is the center). (c) Dilation of A by B.