Abstract
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.
Highlights
The tooth is one of the most important structures in the human mouth
Level set is a classical method that utilize partial differential equations (PDEs) and has been applied in medical images, which provides an implementation of an active contour method based on regions or edges to drive the zero level curve towards the object boundary
Gaussian Filtering Regularized Level Set (GFRLS) method is considered as a region-based active contour model, which shares the advantages of the Chan and Vese (C-V) and Geodesic Active Contour (GAC) models
Summary
There are a lot of diseases with the tooth, and vertical root fracture (VRF) is a severe disease in human tooth. VRF is a common complication in root canaltreated teeth [2, 3]. This leads to major damage to the periodontium. The. GFRLS is one of level set methods and derived from the idea of Chan and Vese (C-V) model and Geodesic Active Contour (GAC) model. GFRLS method is considered as a region-based active contour model, which shares the advantages of the C-V and GAC models. When the initial contour is set inside the desired object, it can expand to the object’s boundary. Let O be a bounded open subset of R2, C(p):
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