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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.