Registration of dental tomographic volume data and scan surface data using dynamic segmentation

Keonhwa Jung, Sukwoo Jung, Inseon Hwang, Taeksoo Kim, Minho Chang

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number1762
JournalApplied Sciences (Switzerland)
Volume8
Issue number10
DOIs
Publication statusPublished - 2018 Sep 29

Fingerprint

computer aided design
artifacts
Computer aided design
Metals
metals
positioning
bundles
Tomography
tomography
industries
sampling
Sampling
Processing
profiles
Industry

Keywords

  • Iterative closest points
  • Local registration
  • Multimodal medical image registration

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

Registration of dental tomographic volume data and scan surface data using dynamic segmentation. / Jung, Keonhwa; Jung, Sukwoo; Hwang, Inseon; Kim, Taeksoo; Chang, Minho.

In: Applied Sciences (Switzerland), Vol. 8, No. 10, 1762, 29.09.2018.

Research output: Contribution to journalArticle

@article{c30aa83d64d449a69d1dbb2b06a0080a,
title = "Registration of dental tomographic volume data and scan surface data using dynamic segmentation",
abstract = "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.",
keywords = "Iterative closest points, Local registration, Multimodal medical image registration",
author = "Keonhwa Jung and Sukwoo Jung and Inseon Hwang and Taeksoo Kim and Minho Chang",
year = "2018",
month = "9",
day = "29",
doi = "10.3390/app8101762",
language = "English",
volume = "8",
journal = "Applied Sciences (Switzerland)",
issn = "2076-3417",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "10",

}

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

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

UR - http://www.scopus.com/inward/citedby.url?scp=85054075019&partnerID=8YFLogxK

U2 - 10.3390/app8101762

DO - 10.3390/app8101762

M3 - Article

VL - 8

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

IS - 10

M1 - 1762

ER -