Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

2014 ◽  
Vol 42 (1) ◽  
pp. 14-27 ◽  
Author(s):  
Yangzhou Gan ◽  
Zeyang Xia ◽  
Jing Xiong ◽  
Qunfei Zhao ◽  
Ying Hu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Computed Tomography Images ◽  
Level Set Model ◽  
Tooth Segmentation

scholarly journals Multi-Projection Segmentation on Dental Cone Beam Computed Tomography Images Using Level Set Method

2021 ◽  
Vol 5 (2) ◽  
pp. 130-139
Author(s):  
Fahmi Syuhada ◽  
Rarasmaya Indraswari ◽  
Agus Zainal Arifin ◽  
Dini Adni Navastara
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Projection Data ◽  
Segmentation Method ◽  
Computed Tomography Images ◽  
Level Set Segmentation ◽  
Level Method ◽  
Sensitivity Specificity

Segmentation of dental Cone-beam computed tomography (CBCT) images based on Boundary Tracking has been widely used in recent decades. Generally, the process only uses axial projection data of CBCT where the slices image that representing the tip of the tooth object have decreased in contrast which impact to difficult to distinguish with background or other elements. In this paper we propose the multi-projection segmentation method by combining the level set segmentation result on three projections to detect the tooth object more optimally. Multiprojection is performed by decomposing CBCT data which produces three projections called axial, sagittal and coronal projections. Then, the segmentation based on the set level method is implemented on the slices image in the three projections. The results of the three projections are combined to get the final result of this method. This proposed method obtains evaluation results of accuracy, sensitivity, specificity with values of 97.18%, 88.62%, and 97.61%, respectively.

3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images

2016 ◽  
Vol 43 (9) ◽  
pp. 5040-5050 ◽  
Author(s):  
Yuru Pei ◽  
Xingsheng Ai ◽  
Hongbin Zha ◽  
Tianmin Xu ◽  
Gengyu Ma
Keyword(s):  
Computed Tomography ◽  
Random Walks ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Computed Tomography Images ◽  
Tooth Segmentation

Segmentation of spinal cord from computed tomography images based on level set method with Gaussian kernel

2020 ◽  
Vol 24 (24) ◽  
pp. 18811-18820
Author(s):  
V. Malathy ◽  
M. Anand ◽  
N. Dayanand Lal ◽  
Zameer Ahmed Adhoni
Keyword(s):  
Computed Tomography ◽  
Spinal Cord ◽  
Level Set Method ◽  
Level Set ◽  
Gaussian Kernel ◽  
Computed Tomography Images
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