Dental non-linear image registration and collection method with 3D reconstruction and change detection

2017 ◽  
Author(s):  
Mark Rahmes ◽  
Dean Fagan ◽  
George Lemieux
Keyword(s):  
Image Registration ◽  
3D Reconstruction ◽  
Change Detection ◽  
Collection Method ◽  
Non Linear

Improved 3D Scene Modeling for Image Registration in Change Detection

2019 ◽  
Vol 2019 (7) ◽  
pp. 465-1-465-7
Author(s):  
Sjors van Riel ◽  
Dennis van de Wouw ◽  
Peter de With
Keyword(s):  
Image Registration ◽  
Change Detection ◽  
Scene Modeling ◽  
3D Scene

Breast image registration by using non-linear local affine transformation

2013 ◽  
Author(s):  
Feiyu Chen ◽  
Peng Zheng ◽  
Penglong Xu ◽  
Andrew D. A. Maidment ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Breast Image ◽  
Non Linear

Sci-YIS Fri - 01: A protocol for the validation of non-linear image registration systems

2005 ◽  
Vol 32 (7Part2) ◽  
pp. 2418-2418
Author(s):  
R Rivest ◽  
T Riauka ◽  
A Murtha ◽  
B Fallone
Keyword(s):  
Image Registration ◽  
Non Linear

scholarly journals Impacts of Thresholds of Gray Value for Cone-Beam Computed Tomography 3D Reconstruction on the Accuracy of Image Matching with Optical Scan

2020 ◽  
Vol 17 (17) ◽  
pp. 6375 ◽  
Author(s):  
Se-Won Park ◽  
Ra Gyoung Yoon ◽  
Hyunwoo Lee ◽  
Heon-Jin Lee ◽  
Yong-Do Choi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
3D Reconstruction ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Dental Arch ◽  
Arch Model ◽  
3D Mesh ◽  
Optical Scan ◽  
Reconstruction Model

In cone-beam computed tomography (CBCT), the minimum threshold of the gray value of segmentation is set to convert the CBCT images to the 3D mesh reconstruction model. This study aimed to assess the accuracy of image registration of optical scans to 3D CBCT reconstructions created by different thresholds of grey values of segmentation in partial edentulous jaw conditions. CBCT of a dentate jaw was reconstructed to 3D mesh models using three different thresholds of gray value (−500, 500, and 1500), and three partially edentulous models with different numbers of remaining teeth (4, 8, and 12) were made from each 3D reconstruction model. To merge CBCT and optical scan data, optical scan images were registered to respective 3D reconstruction CBCT images using a point-based best-fit algorithm. The accuracy of image registration was assessed by measuring the positional deviation between the matched 3D images. The Kruskal–Wallis test and a post hoc Mann–Whitney U test with Bonferroni correction were used to compare the results between groups (α = 0.05). The correlations between the experimental factors were calculated using the two-way analysis of variance test. The positional deviations were lowest with the threshold of 500, followed by the threshold of 1500, and then −500. A significant interaction was found between the threshold of gray values and the number of remaining teeth on the registration accuracy. The most significant deviation was observed in the arch model with four teeth reconstructed with a gray-value threshold of −500. The threshold for the gray value of CBCT segmentation affects the accuracy of image registration of optical scans to the 3D reconstruction model of CBCT. The appropriate gray value that can visualize the anatomical structure should be set, especially when few teeth remain in the dental arch.

Building change detection based on 3D reconstruction

2015 ◽  
Author(s):  
Baohua Chen ◽  
Lei Deng ◽  
Yueqi Duan ◽  
Siyuan Huang ◽  
Jie Zhou
Keyword(s):  
3D Reconstruction ◽  
Change Detection
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