An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.
Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods.
Department of Computer Science, Xiamen University, Xiamen 361005, China.
Liansheng
L
Wang
Department of Computer Science, Xiamen University, Xiamen 361005, China.
Shusheng
S
Li
Department of Computer Science, Xiamen University, Xiamen 361005, China.
Rongzhen
R
Chen
Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112, Taiwan.
Sze-Yu
SY
Liu
Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 112, Taiwan.
Jyh-Cheng
JC
Chen
D000465:Algorithms; D019992:Databases as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D016571:Neural Networks, Computer; D011312:Pressure; D015203:Reproducibility of Results; D014070:Tooth / Q000000981:diagnostic imaging*; D055114:X-Ray Microtomography