TY - JOUR
T1 - Registration of dental tomographic volume data and scan surface data using dynamic segmentation
AU - Jung, Keonhwa
AU - Jung, Sukwoo
AU - Hwang, Inseon
AU - Kim, Taeksoo
AU - Chang, Minho
N1 - Funding Information:
Funding: This research was supported by the Technology Innovation Program (10065150, Development for Low-Cost and Small LIDAR System Technology Based on 3D Laser scanning for 360 Real-time Monitoring), funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and the Korea Evaluation Institute of Industrial Technology (KEIT, Korea).
Publisher Copyright:
© 2018 by the authors.
PY - 2018/9/29
Y1 - 2018/9/29
N2 - Over recent years, computer-aided design (CAD) has become widely used in the dental industry. In dental CAD applications using both volumetric computed tomography (CT) images and 3D optical scanned surface data, the two data sets need to be registered. Previous works have registered volume data and surface data by segmentation. Volume data can be converted to surface data by segmentation and the registration is achieved by the iterative closest point (ICP) method. However, the segmentation needs human input and the results of registration can be poor depending on the segmented surface. Moreover, if the volume data contains metal artifacts, the segmentation process becomes more complex since post-processing is required to remove the metal artifacts, and initially positioning the registration becomes more challenging. To overcome these limitations, we propose a modified iterative closest point (MICP) process, an automatic segmentation method for volume data and surface data. The proposed method uses a bundle of edge points detected along an intensity profile defined by points and normal of surface data. Using this dynamic segmentation, volume data becomes surface data which can be applied to the ICP method. Experimentally, MICP demonstrates fine results compared to the conventional registration method. In addition, the registration can be completed within 10 s if down sampling is applied.
AB - Over recent years, computer-aided design (CAD) has become widely used in the dental industry. In dental CAD applications using both volumetric computed tomography (CT) images and 3D optical scanned surface data, the two data sets need to be registered. Previous works have registered volume data and surface data by segmentation. Volume data can be converted to surface data by segmentation and the registration is achieved by the iterative closest point (ICP) method. However, the segmentation needs human input and the results of registration can be poor depending on the segmented surface. Moreover, if the volume data contains metal artifacts, the segmentation process becomes more complex since post-processing is required to remove the metal artifacts, and initially positioning the registration becomes more challenging. To overcome these limitations, we propose a modified iterative closest point (MICP) process, an automatic segmentation method for volume data and surface data. The proposed method uses a bundle of edge points detected along an intensity profile defined by points and normal of surface data. Using this dynamic segmentation, volume data becomes surface data which can be applied to the ICP method. Experimentally, MICP demonstrates fine results compared to the conventional registration method. In addition, the registration can be completed within 10 s if down sampling is applied.
KW - Iterative closest points
KW - Local registration
KW - Multimodal medical image registration
UR - http://www.scopus.com/inward/record.url?scp=85054075019&partnerID=8YFLogxK
U2 - 10.3390/app8101762
DO - 10.3390/app8101762
M3 - Article
AN - SCOPUS:85054075019
SN - 2076-3417
VL - 8
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 10
M1 - 1762
ER -