Advanced SearchSearch Tips
Markerless Image-to-Patient Registration Using Stereo Vision : Comparison of Registration Accuracy by Feature Selection Method and Location of Stereo Bision System
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Markerless Image-to-Patient Registration Using Stereo Vision : Comparison of Registration Accuracy by Feature Selection Method and Location of Stereo Bision System
Joo, Subin; Mun, Joung-Hwan; Shin, Ki-Young;
  PDF(new window)
This study evaluates the performance of image to patient registration algorithm by using stereo vision and CT image for facial region surgical navigation. For the process of image to patient registration, feature extraction and 3D coordinate calculation are conducted, and then 3D CT image to 3D coordinate registration is conducted. Of the five combinations that can be generated by using three facial feature extraction methods and three registration methods on stereo vision image, this study evaluates the one with the highest registration accuracy. In addition, image to patient registration accuracy was compared by changing the facial rotation angle. As a result of the experiment, it turned out that when the facial rotation angle is within 20 degrees, registration using Active Appearance Model and Pseudo Inverse Matching has the highest accuracy, and when the facial rotation angle is over 20 degrees, registration using Speeded Up Robust Features and Iterative Closest Point has the highest accuracy. These results indicate that, Active Appearance Model and Pseudo Inverse Matching methods should be used in order to reduce registration error when the facial rotation angle is within 20 degrees, and Speeded Up Robust Features and Iterative Closest Point methods should be used when the facial rotation angle is over 20 degrees.
Surgical navigation;Stereo vision;Feature selection;Markerless registration;
 Cited by
Seon-Tae Kim, "Medical Management of Chronic Rhinosinusitis," Korean Journal of Otorhinolaryngology, Vol. 54, no. 11, pp. 746-754, November 2011.

K. K. Min, J. H. Yoon, J. H. Jung, C. Y. Lim, I. G. Kang and S. T. Kim, "The Effect of Intensive Therapy Using Nebulized Antibiotics after Endoscpic Sinus Surgery," Korean Journal of Otorhinolaryngology, Vol. 51, no. 7, pp. 623-629, July 2008.

B. Kim, H. Lee, H. Song, T. Kim, M. Han and G. Oh, "Clinical Manifestations of Intracranial Complication Associated With Paranasal Sinus Infection," Journal of the Korean Neurological Association, Vol. 19, no. 5, pp. 457-463, 2001.

G. Strau, K. Koulechov, S. Rottger, J. Bahner, C. Trantakis, M. Hofer, W. Korb, O. Burgert, J. Meixensberger, D. Manzey, A. Dietz and T. Luth, "Evaluation of a navigation system for ENT with surgical efficiency criteria," The Laryngoscope, vol. 116, no. 4, pp. 564-572, April 2006. crossref(new window)

M. Yin, X. Shen, Y. Hu, and X. Fang, "An Automatic Registration Method Based on Fiducial Marker for Image Guided Neurosurgery System," AsiaSim 2013. Springer Berlin Heidelberg, pp. 114-125, 2013.

이종원, 정완균, "차세대 척추수술 로봇 기술의 현황과 전망," 대한전자공학회, 제38권, 제11호, 55-60쪽, 2011년 11월.

J. Wang, H. Suenaga, K. Hoshi, L. Yang, E. Kobayashi, I. Sakuma, and H. Liao, "Augmented reality navigation with automatic marker-free image registration using 3-D image overlay for dental surgery," Biomedical Engineering, IEEE Transactions on, Vol. 61, no. 4, pp. 1295-1304, 2014. crossref(new window)

J. D. Lee, C. H. Huang, T. C. Huang, H. Y. Hsieh, and S. T. Lee, "Medical augment reality using a markerless registration framework," Expert Systems with Applications, Vol. 39, no. 5, pp. 5286-5294, 2012. crossref(new window)

O. Makiese, P. Pillai, A. Salma, S. Sammet, and M. Ammirati, "Accuracy validation in a cadaver model of cranial neuronavigation using a surface autoregistration mask." Neurosurgery, Vol. 67, no. 3, ons85-ons90, 2010.

Bilsky, M. H., Bentz, B., Vitaz, T., Shah, J., & Kraus, D. "Craniofacial resection for cranial base malignancies involving the infratemporal fossa," Neurosurgery, Vol. 57, (4 Suppl), pp. 339-347, 2005.

P. Cappabianca, L. Califano, and G. Iaconetta, "Cranial, craniofacial and skull base surgery," Springer, 2010.

C. Harris, and M. Stephens, "A combined corner and edge detector," In Alvey vision conference, Vol. 15, pp. 50, August 1988.

H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," In Computer vision-ECCV 2006 (pp. 404-417). Springer Berlin Heidelberg, 2006.

Y. Pang, W. Li, Y. Yuan, and J. Pan, "Fully affine invariant SURF for image matching," Neurocomputing, Vol. 85, pp. 6-10, 2012. crossref(new window)

G. Tzimiropoulos, and M. Pantic, "Optimization problems for fast aam fitting in-the-wild," In Computer Vision (ICCV), 2013 IEEE International Conference on (pp. 593-600), IEEE, December 2013.

B. Lee, C. Kim and R. Park, "Derivation of a Confidence Matrix for Orientation Components in the ICP Algorithm," Journal of the Korean Institute of Telematics and Electronics S. S, Vol. 35, no. 12, pp. 69-76, September 1998.

M. Staroswiecki, "Fault tolerant control: the pseudo-inverse method revisited," In 23h IFAC World Congress (No. 2), July 2005.

K. Y. Shin, and J. H. Mun, "A multi-camera calibration method using a 3-axis frame and wand," International Journal of Precision Engineering and Manufacturing, Vol. 13, no. 2, pp. 283-289, 2012. crossref(new window)

S. Lee, Y. Han and H. Hahn, "A New Stereo Matching Method based on Reliability Space," Journal of The Institute of Electronics Engineers of Korea SP, Vol. 6, pp. 82-90, November 2010.

Melfi, R., Kondra, S., & Petrosino, A. (2013). Human activity modeling by spatio temporal textural appearance. Pattern Recognition Letters, 34(15), 1990-1994. crossref(new window)

A. Ahmadian, A. F. Kazerooni, S. Mohagheghi, K. A. Khoiy, and M. S. Hosseini, "A region-based anatomical landmark configuration for sinus surgery using image guided navigation system: A phantom-study," Journal of Cranio-Maxillofacial Surgery, Vol. 42, no. 6, pp. 816-824, 2014. crossref(new window)

T. Sim, H. Kwon, S. E. Oh, S. B. Joo, A. Choi, H. M. Heo, K. Kisun, and J. H. Mun, "Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network," Journal of biomechanical engineering, Vol. 137, no. 9, 2015.

T. F. Cootes, G. V. Wheeler, K. N. Walker, and C. J. Taylor, "View-based active appearance models," Image and vision computing, Vol. 20, no. 9, pp. 657-664, 2002. crossref(new window)