Document Type : Original Paper
Authors
1
Ph.D., Student, Faculty of Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran
2
Associate Professor, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
3
Assistant Professor, Faculty of Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran
4
Assistant Professor, Oral & Maxillofacial Surgery Department, Faculty of Dentistry Medical Science of Tehran University, Tehran, Iran
Abstract
Introduction: Various computer assisted medical procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries require automatic quantification and volumetric visualization of teeth. In this regard, segmentation is a major step.
Material and Methods: In this paper, inspired by our previous experiences and considering the anatomical knowledge of teeth and jaws, we propose a hybrid technique for teeth segmentation and visualization in CT volumetric data. The major steps of the proposed techniques are as follows: (1) Separation of teeth in CT dataset; (2) Initial segmentation of teeth in panoramic projection; (3) Final segmentation of teeth in CT dataset; (4) 3D visualization of teeth.
Results: The proposed algorithm was evaluated in 30 multi-slice CT datasets. Segmented images were compared with manually outlined contours. In order to evaluate the proposed method, we utilized several common performance measures such as sensitivity, specificity, precision, accuracy and mean error rate. The experimental results reveal the effectiveness of the proposed method.
Discussion and Conclusion: In the proposed algorithm, the variationallevel set technique was utilized to trace the contour of the teeth. In view of the fact that this technique is based on the characteristics of the overall region of the tooth image, it is possible to extract a very smooth and accurate tooth contour using this technique. For the available datasets, the proposed technique was more successful in teeth segmentation compared to previous techniques.
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