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Automated Teeth Detection and Numbering in Dental Radiographs
Introduction to Teeth Detection and Numbering
Teeth detection and numbering in dental radiographs are crucial for diagnostic and treatment planning in dentistry. Automated systems leveraging advanced algorithms and deep learning models have shown promising results in enhancing the accuracy and efficiency of these tasks.
Bayesian Classification for Teeth Numbering
Bayesian classification has been effectively used to classify and number teeth in bitewing dental images. This method utilizes Fourier descriptors of teeth contours to distinguish between molars and premolars. The spatial relationship between these teeth types is then used to assign absolute numbers, correcting any misclassifications to achieve high precision .
Image Enhancement and SVM for Teeth Classification
An effective system for dental bitewing radiographs involves image enhancement techniques such as homomorphic filtering and adaptive morphological transformation to improve contrast and illumination. Teeth are isolated into regions of interest (ROIs) using iterative thresholding and integral projection. A binary linear support vector machine (SVM) classifies teeth based on features like relative length/width ratios and crown size. This system achieves high accuracy rates for both classification and numbering.
Convolutional Neural Networks (CNNs) for Panoramic Radiographs
CNNs have been widely adopted for teeth detection and numbering in panoramic radiographs. A notable approach uses Faster R-CNN for teeth detection and VGG-16 for numbering, achieving performance metrics comparable to expert-level accuracy. This method simplifies the process of filling out digital dental charts, saving time and improving record completeness .
Deep Learning for Pediatric Teeth Numbering
Deep learning models, such as those based on ResNet-50, have been developed to identify tooth number abnormalities in pediatric panoramic radiographs. These models can detect additional teeth and identify missing permanent teeth, assisting in diagnosing tooth number abnormalities with high sensitivity and specificity .
Impact of Image Context on Classification Accuracy
The accuracy of deep learning models for tooth classification improves with increased image context. Studies have shown that expanding the context around each tooth segment significantly enhances classification accuracy, particularly for complex cases involving multiple teeth and interdental spaces.
Conclusion
Automated systems for teeth detection and numbering in dental radiographs, utilizing Bayesian classification, SVM, and CNNs, have demonstrated high accuracy and efficiency. These advancements hold significant potential for practical applications in clinical dentistry, aiding in diagnostic processes and improving the completeness of dental records.
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