A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.
Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled 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 3 dimensional (3D) information, and classify the tooth by employing unsupervised learning i.e., k-means++ method. In order to evaluate the proposed method, the experiments are conducted on the sufficient and extensive datasets of mandibular molars. The experimental results show that our method can achieve higher accuracy and robustness compared to other three clustering methods.
Department of Computer Science, Xiamen University, Xiamen 361005, China. Electronic address: lswang@xmu.edu.cn.
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
D016000:Cluster Analysis; D003799:Dental Stress Analysis / Q000379:methods; D020342:Finite Element Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008963:Molar / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D016011:Normal Distribution; D000069558:Unsupervised Machine Learning; D055114:X-Ray Microtomography / Q000379:methods*
Classification; K-means++; Level set; Tooth