Paper
18 March 2015 Surgical tool detection and tracking in retinal microsurgery
Author Affiliations +
Abstract
Visual tracking of surgical instruments is an essential part of eye surgery, and plays an important role for the surgeons as well as it is a key component of robotics assistance during the operation time. The difficulty of detecting and tracking medical instruments in-vivo images comes from its deformable shape, changes in brightness, and the presence of the instrument shadow. This paper introduces a new approach to detect the tip of surgical tool and its width regardless of its head shape and the presence of the shadows or vessels. The approach relies on integrating structural information about the strong edges from the RGB color model, and the tool location-based information from L*a*b color model. The probabilistic Hough transform was applied to get the strongest straight lines in the RGB-images, and based on information from the L* and a*, one of these candidates lines is selected as the edge of the tool shaft. Based on that line, the tool slope, the tool centerline and the tool tip could be detected. The tracking is performed by keeping track of the last detected tool tip and the tool slope, and filtering the Hough lines within a box around the last detected tool tip based on the slope differences. Experimental results demonstrate the high accuracy achieved in term of detecting the tool tip position, the tool joint point position, and the tool centerline. The approach also meets the real time requirements.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mohamed Alsheakhali, Mehmet Yigitsoy, Abouzar Eslami, and Nassir Navab "Surgical tool detection and tracking in retinal microsurgery", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 941511 (18 March 2015); https://doi.org/10.1117/12.2082335
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Cited by 7 scholarly publications.
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KEYWORDS
Surgery

Image segmentation

Edge detection

Hough transforms

Microsurgery

RGB color model

Optical tracking

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