Imaging plays an important role in all clinical processes. One challenge in medical image data processing is detection and tracking objects and instruments, which faces complications arising from the developed medical image acquisition systems and also the nature of in-vivo medical images. Special properties of the in-vivo bio images such as noise, specular highlights, inhomogeneity, heterogeneity, varying luminosity, and background change, in addition to the changes of camera, out of camera view tools, and multiple moving tools (instrument tools, surgical suture, cutting instrument, tissue movement) make object detection and tracking in the biomedical image processing complicated. In this study, the k-means clustering method in combination with the level set active curve model are used to develop a platform for low-cost tracking of surgical tools in robotic surgery videos. After removing the image background, the smoothed image is used as input to the numerical method. This model tracks the robot tools even when the camera view changes, the tool is lost, the tissue is bleeding and moving, and the luminosity of the images changes. The developed model is validated using video frames of real and simulated robotic surgeries. The accuracy of model in tracking da vinci robot end-effectors for a video with 12000 frames, recorded at Roswell Park Cancer Institute, is 93%. Accuracy of proposed framework is compared to those for existing numerical models, DRLSE and Chan-Vese. The results show that proposed surgical robot tool tracking model is more efficient than existing computational models.
Skip Nav Destination
ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 6–9, 2017
Cleveland, Ohio, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5817-2
PROCEEDINGS PAPER
Use of Numerical-Clustering Framework for End-Effector Tracking During Robot-Assisted Surgery
Somayeh B. Shafiei,
Somayeh B. Shafiei
University at Buffalo, SUNY, Buffalo, NY
Search for other works by this author on:
Khurshid A. Guru
Khurshid A. Guru
Roswell Park Cancer Institute, Buffalo, NY
Search for other works by this author on:
Somayeh B. Shafiei
University at Buffalo, SUNY, Buffalo, NY
Khurshid A. Guru
Roswell Park Cancer Institute, Buffalo, NY
Paper No:
DETC2017-67616, V05AT08A063; 7 pages
Published Online:
November 3, 2017
Citation
Shafiei, SB, & Guru, KA. "Use of Numerical-Clustering Framework for End-Effector Tracking During Robot-Assisted Surgery." Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 5A: 41st Mechanisms and Robotics Conference. Cleveland, Ohio, USA. August 6–9, 2017. V05AT08A063. ASME. https://doi.org/10.1115/DETC2017-67616
Download citation file:
20
Views
Related Proceedings Papers
Related Articles
Multifunctional Articulating Surgical Robot for NOTES
J. Med. Devices (June,2011)
Design Optimization of Single-Port Minimally Invasive Intervention Devices
J. Med. Devices (June,2009)
Design of a Bending Section Auto-Driven Mechanism for Colonoscope
J. Med. Devices (June,2009)
Related Chapters
Introduction and Scope
High Frequency Piezo-Composite Micromachined Ultrasound Transducer Array Technology for Biomedical Imaging
Conclusion & executive summary
Photodynamic Therapy Mediated by Fullerenes and their Derivatives
Time-Varying Coefficient Aided MM Scheme
Robot Manipulator Redundancy Resolution