Authors:
Jiawei Zhou
and
Shahram Payandeh
Affiliation:
Simon Fraser University, Canada
Keyword(s):
Visual Tracking, Minimally Invasive Surgery, Adaptive Gaussian Mixture Model, Particle Filter.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Tracking and Visual Navigation
Abstract:
Tracking surgical tools in mono-endoscopic surgery can offer a conventional (non-robotics) application of this type of procedure a versatile surgeon-computer interface. For example, tracking the surgical tools can enable the surgeon to interact with the overlaid menu which allows them to have access to medical information of the patient. Another example is the capability that such tracking can offer where the surgeon through surgical tool can manually register per-operative images of the patient approach on the surgical site. This paper presents the results of some of the tracking schemes which we have explored and analysed as a part of our studies. Tracking framework based on both Gaussian and non-Gaussian framework are explored and compared. Although majority of the approaches can offer a robust performance when used in the real surgical scene, the method based on Particle Filter is found to have a better success rate. Based on these experimental results, the paper also offers some
discussions and suggestions for future research.
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