EurekaMag.com logo
+ Site Statistics
References:
53,869,633
Abstracts:
29,686,251
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system



A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system



Computer Assisted Surgery 22(Sup1): 319-325



Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty. A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration. Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18-26 mm) and different support radii (2-6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm. This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

(PDF emailed within 0-6 h: $19.90)

Accession: 059363588

Download citation: RISBibTeXText

PMID: 29094615

DOI: 10.1080/24699322.2017.1389411



Related references

Automatic Masking for Robust 3D-2D Image Registration in Image-Guided Spine Surgery. Proceedings of Spie--the International Society for Optical Engineering 9786(): -, 2016

Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Medical Physics 35(10): 4612-4624, 2008

Application accuracy in frameless image-guided neurosurgery: a comparison study of three patient-to-image registration methods. Journal of Neurosurgery 106(6): 1012-1016, 2007

A novel registration method for image-guided neurosurgery system based on stereo vision. Bio-Medical Materials and Engineering 26 Suppl 1: S967-S973, 2016

Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE Transactions on Medical Imaging 34(2): 366-380, 2015

Markerless patient registration. A new technique for image-guided surgery of the lateral base of the skull. Hno 53(2): 148-154, 2004

A new markerless patient-to-image registration method using a portable 3D scanner. Medical Physics 41(10): 101910-101910, 2015

Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery. Medical Physics 41(10): 102302-102302, 2015

Multimodal image re-registration via mutual information to account for initial tissue motion during image-guided neurosurgery. Conference Proceedings 3: 1675-1678, 2007

An accurate and ergonomic method of registration for image-guided neurosurgery. Computerized Medical Imaging & Graphics. 18(4): 273-277, 1994

Automated Fiducial Marker Detection for Patient Registration in Image-Guided Neurosurgery. Computer Aided Surgery 8(1): 17-23, 2003

Automated fiducial marker detection for patient registration in image-guided neurosurgery. Computer Aided Surgery 8(1): 17-23, 2004

A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery. Medical Physics 35(5): 2180-2194, 2008

Automatic 3D ultrasound calibration for image guided therapy using intramodality image registration. Physics in Medicine and Biology 58(21): 7481-7496, 2014

A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery. IEEE Transactions on Information Technology in Biomedicine 13(6): 976-983, 2010