A monocular SLAM system based on SIFT features for gastroscope tracking.

Wang, Y; Zhao, L; Gong, L; Chen, X; Zuo, S

A monocular SLAM system based on SIFT features for gastroscope tracking.

Wang, Y Zhao, L

Gong, L Chen, X Zuo, S

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Med Biol Eng Comput, 2023, 61, (2), pp. 511-523
Issue Date:: 2023-02

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Gong, L
dc.contributor.author	Chen, X
dc.contributor.author	Zuo, S
dc.date.accessioned	2024-04-29T09:00:57Z
dc.date.available	2022-12-09
dc.date.available	2024-04-29T09:00:57Z
dc.date.issued	2023-02
dc.identifier.citation	Med Biol Eng Comput, 2023, 61, (2), pp. 511-523
dc.identifier.issn	0140-0118
dc.identifier.issn	1741-0444
dc.identifier.uri	http://hdl.handle.net/10453/178477
dc.description.abstract	During flexible gastroscopy, physicians have extreme difficulties to self-localize. Camera tracking method such as simultaneous localization and mapping (SLAM) has become a research hotspot in recent years, allowing tracking of the endoscope. However, most of the existing solutions have focused on tasks in which sufficient texture information is available, such as laparoscope tracking, and cannot be applied to gastroscope tracking since gastroscopic images have fewer textures than laparoscopic images. This paper proposes a new monocular SLAM framework based on scale-invariant feature transform (SIFT) and narrow-band imaging (NBI), which extracts SIFT features instead of oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) features from gastroscopic NBI images, and performs feature retention based on the response sorting strategy for achieving more matches. Experimental results show that the root mean squared error of the proposed algorithm can reach a minimum of 2.074 mm, and the pose accuracy can be improved by up to 25.73% compared with oriented FAST and rotated BRIEF (ORB)-SLAM. SIFT features and response sorting strategy can achieve more accurate matching in gastroscopic NBI images than other features and homogenization strategy, and the proposed algorithm can also run successfully on real clinical gastroscopic data. The proposed algorithm has the potential clinical value to assist physicians in locating the gastroscope during gastroscopy.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Springer Nature
dc.relation.ispartof	Med Biol Eng Comput
dc.relation.isbasedon	10.1007/s11517-022-02739-1
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.subject.mesh	Gastroscopes
dc.subject.mesh	Algorithms
dc.subject.mesh	Gastroscopes
dc.subject.mesh	Algorithms
dc.subject.mesh	Gastroscopes
dc.subject.mesh	Algorithms
dc.title	A monocular SLAM system based on SIFT features for gastroscope tracking.
dc.type	Journal Article
utslib.citation.volume	61
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - RI - Robotics Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-29T09:00:55Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	61
utslib.citation.issue	2

Abstract:

During flexible gastroscopy, physicians have extreme difficulties to self-localize. Camera tracking method such as simultaneous localization and mapping (SLAM) has become a research hotspot in recent years, allowing tracking of the endoscope. However, most of the existing solutions have focused on tasks in which sufficient texture information is available, such as laparoscope tracking, and cannot be applied to gastroscope tracking since gastroscopic images have fewer textures than laparoscopic images. This paper proposes a new monocular SLAM framework based on scale-invariant feature transform (SIFT) and narrow-band imaging (NBI), which extracts SIFT features instead of oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) features from gastroscopic NBI images, and performs feature retention based on the response sorting strategy for achieving more matches. Experimental results show that the root mean squared error of the proposed algorithm can reach a minimum of 2.074 mm, and the pose accuracy can be improved by up to 25.73% compared with oriented FAST and rotated BRIEF (ORB)-SLAM. SIFT features and response sorting strategy can achieve more accurate matching in gastroscopic NBI images than other features and homogenization strategy, and the proposed algorithm can also run successfully on real clinical gastroscopic data. The proposed algorithm has the potential clinical value to assist physicians in locating the gastroscope during gastroscopy.

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http://hdl.handle.net/10453/178477