An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images

• Published in: PloS one Vol. 11; no. 6; p. e0157694
• Main Authors: Wang, Liansheng, Li, Shusheng, Chen, Rongzhen, Liu, Sze-Yu, Chen, Jyh-Cheng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.06.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial neural networks; Bioinformatics; Biology and Life Sciences; Classification; Clustering; Computed tomography; Computer science; Databases as Topic; Datasets; Dentistry; Doctors; Engineering and Technology; Filtration; Fourier transforms; Fractures; Humans; Image classification; Image processing; Image Processing, Computer-Assisted; Image segmentation; Imaging, Three-Dimensional; Mandible; Medical imaging; Medical personnel; Medicine and Health Sciences; Molars; Neural networks; Neural Networks (Computer); Pattern recognition; Physical Sciences; Physicians; Physiological aspects; Pressure; Reproducibility of Results; Research and Analysis Methods; Segmentation; Stress analysis; Teeth; Therapeutic applications; Tomography; Tooth - diagnostic imaging; X-Ray Microtomography; Iran; China; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0157694

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.