Machine Vision for the Inspection of Surgical Tasks: Applications to Robotic Surgery Systems
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:51110", author = "M. Ovinis and D. Kerr and K. Bouazza-Marouf and M. Vloeberghs", title = "Machine Vision for the Inspection of Surgical Tasks: Applications to Robotic Surgery Systems", abstract = "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.", volume = "4", number = "9", pages = "404-7", }