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

Autor: Wang Y; Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China., Zhao L; Faculty of Engineering and Information Technology, Robotics Institute, University of Technology Sydney, Sydney, Australia., Gong L; Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China., Chen X; Tianjin Medical University General Hospital, Tianjin, China., Zuo S; Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China. siyang_zuo@tju.edu.cn.
Jazyk: angličtina
Zdroj: Medical & biological engineering & computing [Med Biol Eng Comput] 2023 Feb; Vol. 61 (2), pp. 511-523. Date of Electronic Publication: 2022 Dec 19.
DOI: 10.1007/s11517-022-02739-1
Abstrakt: 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.
(© 2022. International Federation for Medical and Biological Engineering.)
Databáze: MEDLINE
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