A level-set based approach for anterior teeth segmentation in cone beam computed tomography images.
Cone beam CT (CBCT) has gained popularity in dentistry for 3D imaging of the jaw bones and teeth due to its high resolution and relatively lower radiation exposure compared to multi-slice CT (MSCT). However, image segmentation of the tooth from CBCT is more complex than from MSCT due to lower bone signal-to-noise. This paper describes a level-set method to extract tooth shape from CBCT images of the head. We improve the variational level set framework with three novel energy terms: (1) dual intensity distribution models to represent the two regions inside and outside the tooth; (2) a robust shape prior to impose a shape constraint on the contour evolution; and (3) using the thickness of the tooth dentine wall as a constraint to avoid leakage and shrinkage problems in the segmentation process. The proposed method was compared with several existing methods and was shown to give improved segmentation accuracy.
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Faculty of Dentistry, National University of Singapore, Singapore.
Dong Xu
DX
Ji
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
Sim Heng
SH
Ong
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Faculty of Dentistry, National University of Singapore, Singapore. Electronic address: kelvinfoong@nuhs.edu.sg.
Kelvin Weng Chiong
KW
Foong
D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007568:Jaw / Q000000981:diagnostic imaging*; D016011:Normal Distribution; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D014070:Tooth / Q000000981:diagnostic imaging*; D019228:Tooth Crown / Q000473:pathology
3D imaging; Anterior teeth; Cone beam computed tomography; Level set; Tooth segmentation