International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Tooth Contour Extraction for Matching Dental Radiographs
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Coupled Parametric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D statistical models for tooth surface reconstruction
Computers in Biology and Medicine
Modeling interaction for segmentation of neighboring structures
IEEE Transactions on Information Technology in Biomedicine
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
IEEE Transactions on Image Processing
A new visualization framework of dental CT images for orthodontics
Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry
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3D visualization of teeth from CT images provides important assistance for dentists performing orthodontic surgery and treatment. However, dental CT images present several major challenges for the segmentation of tooth, which touches with adjacent teeth as well as surrounding periodontium and jaw bones. Moreover, tooth contour suffers from topological changes and splits into several branches. In this work, we focus on the segmentation of individual teeth with complete crown and root parts. To this end, we propose adaptive active contour tracking algorithms: single level set method tracking for root segmentation to handle the complex image conditions as well as the root branching problem, and coupled level set method tracking for crown segmentation in order to separate the touching teeth and create the virtual common boundaries between them. Furthermore, we improve the variational level set method in several aspects: gradient direction is introduced into the level set framework to prevent catching the surrounding object boundaries; in addition to the shape prior, intensity prior is introduced to provide adaptive shrinking or expanding forces in order to deal with the topological changes. The test results for both tooth segmentation and 3D reconstruction show that the proposed method can visualize individual teeth with high accuracy and efficiency.