Machine Vision for the Inspection of Surgical Tasks: Applications to Robotic Surgery Systems
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32870
Machine Vision for the Inspection of Surgical Tasks: Applications to Robotic Surgery Systems

Authors: M. Ovinis, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs


The use of machine vision to inspect the outcome of surgical tasks is investigated, with the aim of incorporating this approach in robotic surgery systems. Machine vision is a non-contact form of inspection i.e. no part of the vision system is in direct contact with the patient, and is therefore well suited for surgery where sterility is an important consideration,. As a proof-of-concept, three primary surgical tasks for a common neurosurgical procedure were inspected using machine vision. Experiments were performed on cadaveric pig heads to simulate the two possible outcomes i.e. satisfactory or unsatisfactory, for tasks involved in making a burr hole, namely incision, retraction, and drilling. We identify low level image features to distinguish the two outcomes, as well as report on results that validate our proposed approach. The potential of using machine vision in a surgical environment, and the challenges that must be addressed, are identified and discussed.

Keywords: Visual inspection, machine vision, robotic surgery.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1760


[1] C. W. Burckhardt, P. Flury and D. Glauser, "Stereotactic brain surgery," Engineering in Medicine and Biology Magazine, IEEE, vol. 14, pp. 314- 317, 1995.
[2] N. Villotte, D. Glauser, P. Flury and C. W. Burckhardt, "Conception of stereotactic instruments for the neurosurgical robot minerva," in Engineering in Medicine and Biology Society, Vol.14. Proceedings of the Annual International Conference of the IEEE, 1992, pp. 1089-1090.
[3] H. Fankhauser, D. Glauser, P. Flury, Y. Piguet, M. Epitaux, J. Favre and R. A. Meuli, "Robot for CT-guided stereotactic neurosurgery," Stereotact. Funct. Neurosurg., vol. 63, pp. 93-98, 1994.
[4] D. Glauser, H. Fankhauser, M. Epitaux, J. L. Hefti and A. Jaccottet, "Neurosurgical robot Minerva: first results and current developments," J. Image Guid. Surg., vol. 1, pp. 266-272, 1995.
[5] B. P. L. Lo, A. Darzi and G. Z. Yang, "Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues," Medical Image Computing and Computer-Assisted Intervention: MICCAI .International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 230-237, 2003.
[6] N. Padoy, T. Blum, H. Feussner, M. O. Berger and N. Navab, "On-line recognition of surgical activity for monitoring in the operating room," in Proceedings of 20th Conference on Innovative Applications of Artificial Intelligence (IAAI), 2008.
[7] R. A. Rival, O. M. Antonyshyn, J. H. Phillips and C. Y. Pang, "Temporal fascial periosteal and musculoperiosteal flaps in the pig: Design and blood flow inspection," J. Craniofac. Surg., vol. 6, pp. 466, 1995.
[8] G. M. Kaiser and N. R. Fruhauf, "Method of intracranial pressure monitoring and cerebrospinal fluid sampling in swine," Laboratory Animals(London), vol. 41, pp. 80-85, 2007.
[9] J. F. M. Manschot and A. J. M. Brakkee, "The measurement and modelling of the mechanical properties of human skin in vivo--I. The measurement," J. Biomech., vol. 19, pp. 511-515, 1986.
[10] P. Wellner, "Adaptive thresholding for the DigitalDesk," Xerox, EPC1993-110, 1993.
[11] K. Zuiderveld, "Contrast Limited Adaptive Histograph Equalization." in Graphic Gems IV. San Diego: Academic Press Professional, 1994, pp. 474-485.
[12] T. Ohashi, Z. Aghbari and A. Makinouchi, "Hill-climbing algorithm for efficient color-based image segmentation." in IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, 2003, pp. 17-22.
[13] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features of Image Classification," IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-3, no. 6, Nov. 1973.
[14] R. C. Gonzalez, & R. E. Woods, Digital Image Processing, Pearson Prentice Hall, pp. 828-842.