Robust camera localisation with depth reconstruction for bronchoscopic navigation
File(s)
Accepted version
Author(s)
Shen, M
Giannarou, S
Yang, G-Z
Type
Journal Article
Abstract
Purpose
Bronchoscopy is a standard technique for airway examination, providing a minimally invasive approach for both diagnosis and treatment of pulmonary diseases. To target lesions identified pre-operatively, it is necessary to register the location of the bronchoscope to the CT bronchial model during the examination. Existing vision-based techniques rely on the registration between virtually rendered endobronchial images and videos based on image intensity or surface geometry. However, intensity-based approaches are sensitive to illumination artefacts, while gradient-based approaches are vulnerable to surface texture.
Methods
In this paper, depth information is employed in a novel way to achieve continuous and robust camera localisation. Surface shading has been used to recover depth from endobronchial images. The pose of the bronchoscopic camera is estimated by maximising the similarity between the depth recovered from a video image and that captured from a virtual camera projection of the CT model. The normalised cross-correlation and mutual information have both been used and compared for the similarity measure.
Results
The proposed depth-based tracking approach has been validated on both phantom and in vivo data. It outperforms the existing vision-based registration methods resulting in smaller pose estimation error of the bronchoscopic camera. It is shown that the proposed approach is more robust to illumination artefacts and surface texture and less sensitive to camera pose initialisation.
Conclusions
A reliable camera localisation technique has been proposed based on depth information for bronchoscopic navigation. Qualitative and quantitative performance evaluations show the clinical value of the proposed framework.
Bronchoscopy is a standard technique for airway examination, providing a minimally invasive approach for both diagnosis and treatment of pulmonary diseases. To target lesions identified pre-operatively, it is necessary to register the location of the bronchoscope to the CT bronchial model during the examination. Existing vision-based techniques rely on the registration between virtually rendered endobronchial images and videos based on image intensity or surface geometry. However, intensity-based approaches are sensitive to illumination artefacts, while gradient-based approaches are vulnerable to surface texture.
Methods
In this paper, depth information is employed in a novel way to achieve continuous and robust camera localisation. Surface shading has been used to recover depth from endobronchial images. The pose of the bronchoscopic camera is estimated by maximising the similarity between the depth recovered from a video image and that captured from a virtual camera projection of the CT model. The normalised cross-correlation and mutual information have both been used and compared for the similarity measure.
Results
The proposed depth-based tracking approach has been validated on both phantom and in vivo data. It outperforms the existing vision-based registration methods resulting in smaller pose estimation error of the bronchoscopic camera. It is shown that the proposed approach is more robust to illumination artefacts and surface texture and less sensitive to camera pose initialisation.
Conclusions
A reliable camera localisation technique has been proposed based on depth information for bronchoscopic navigation. Qualitative and quantitative performance evaluations show the clinical value of the proposed framework.
Date Issued
2015-04-23
Date Acceptance
2015-03-25
Citation
International Journal of Computer Assisted Radiology and Surgery, 2015, 10 (6), pp.801-813
ISSN
1861-6410
Publisher
Springer Verlag
Start Page
801
End Page
813
Journal / Book Title
International Journal of Computer Assisted Radiology and Surgery
Volume
10
Issue
6
Copyright Statement
© 2015 CARS. The final publication is available at Springer via http://dx.doi.org/10.1007/s11548-015-1197-y
Sponsor
Katholieke Universiteit Leuven
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000355422800013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
CASCADE - 601021
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Surgery
Engineering
Bronchoscopic navigation
2D/3D registration
Shape from shading
Depth recovery
IMAGE REGISTRATION
VIDEO TRACKING
SHAPE
REAL
VALIDATION
Algorithms
Bronchoscopes
Bronchoscopy
Humans
Imaging, Three-Dimensional
Lighting
Reproducibility of Results
Nuclear Medicine & Medical Imaging
1103 Clinical Sciences
Publication Status
Published
Coverage Spatial
Barcelona, SPAIN