Classification and numbering of teeth in dental bitewing images M. H. Mahoor and M. Abdel-Mottaleb Pattern Recognition, Vol. 38, No. 4, pp. 577-586, April 2005. Speaker: Cheng-Hsiung Li Date: 2005-06-02

Outline Introduction Method Feature extraction and pre-classification Final classification and numbering Experiments and results Conclusion

Introduction - ADIS An automated dental identification system Bitewing Segmentation Feature extraction and search Bitewing * Bitewing of X-ray image (X光的照片) DB Identification Somebody of death Missing people

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

Method – Adult dentition system The adult dentition contains 32 teeth, 16 teeth in each jaw. molars premolars

Method – teeth segmentation First method -Segmentation Second method -Segmentation Segmentation Feature extraction Classification

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). X jy(n) u(n) = x(n) + jy(n), n = 0,1,…,N-1 *DFT(傅立葉轉換)->高頻對應細節，低頻則是決定整體的形狀 Fourier transform to above complex signal Fourier coefficients: Segmentation Feature extraction Classification

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. (xc, yc) Fourier coefficients: Segmentation Feature extraction Classification

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 Say c2 Say c1 P(x|c1) P(x|c2) P(x|ci) Segmentation Feature extraction Classification

Final classification and numbering Arrangement of teeth in dental bitewing images. (a) left quadrant (b) right quadrant. (a) (b) (c) (d) * When the confidence measure of the tooth is greater than threshold and their class membership are different, then we consider the tooth with low confidence measure as a misclassified tooth and assign it to the same class. Classification and numbering of the teeth in dental bitewing images. (c) left quadrant (d) right quadrant

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.

Experiments and results-(2) Pre-classification of teeth using first method of segmentation Pre-classification of teeth using second method of segmentation

Experiments and results-(3) Final classification of teeth using first method of segmentation Final classification of teeth using second method of segmentation

Experiments and results-(4) Missing teeth Missclassification teeth

Conclusion The authors introduced a method for robust classification and numbering of molar and premolar teeth in bitewing images using Bayesian classification.

Distinguish between method 1 and method 2 (c) (d) (e) (f) (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. Source: Automatic Human Identification based on Dental X-Ray Images

Fourier coefficients Fourier transform (DFT) Fourier transform (DFT) … Original image (S = 64) P = 2 P = 62 P = 64

Morphological image processing Dilation d (c) d/8 d (a) . d/4 (b) (a) Set A. (b) Square structuring element (dot is the center). (c) Dilation of A by B.