Machine Learning
Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Image Processing - Principles and Applications
Image Processing - Principles and Applications
A new shape descriptor defined on the Radon transform
Computer Vision and Image Understanding
Classification and numbering of teeth in dental bitewing images
Pattern Recognition
A system for human identification from X-ray dental radiographs
Pattern Recognition
Teeth segmentation in digitized dental X-ray films using mathematical morphology
IEEE Transactions on Information Forensics and Security
Fusion of Matching Algorithms for Human Identification Using Dental X-Ray Radiographs
IEEE Transactions on Information Forensics and Security
Automatic detection and classification of teeth in CT data
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Teeth/Palate and interdental segmentation using artificial neural networks
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
3D dental biometrics: Alignment and matching of dental casts for human identification
Computers in Industry
Teeth segmentation of dental periapical radiographs based on local singularity analysis
Computer Methods and Programs in Biomedicine
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We propose a dental classification and numbering system to effectively segment, classify, and number teeth in dental bitewing radiographs. An image enhancement method that combines homomorphic filtering, homogeneity-based contrast stretching, and adaptive morphological transformation is proposed to improve both contrast and illumination evenness of the radiographs simultaneously. Iterative thresholding and integral projection are adapted to isolate teeth to regions of interest (ROIs) followed by contour extraction of the tooth and the pulp (if available) from each ROI. A binary linear support vector machine using the skew-adjusted relative length/width ratios of both teeth and pulps, and crown size as features is proposed to classify each tooth to molar or premolar. Finally, a numbering scheme that combines a missing teeth detection algorithm and a simplified version of sequence alignment commonly used in bioinformatics is presented to assign each tooth a proper number. Experimental results show that our system has accuracy rates of 95.1% and 98.0% for classification and numbering, respectively, in terms of number of teeth tested, and correctly classifies and numbers the teeth in four images that were reported either misclassified or erroneously numbered, respectively.