Abstract
This paper compares Kanade-Lucas-Tomasi (KLT), speeded up robust feature (SURF), and scale invariant feature transformation (SIFT) features applied to bronchoscope tracking. In our study, we first use KLT, SURF, or SIFT features and epipolar constraints to obtaininter-frame translation (up to scale) and orientation displacements and Kalman filtering to recover an estimate for the magnitude of the motion (scale factor determination), and then multiply inter-frame motion parameters onto the previous pose of the bronchoscope camera to achieve the predicted pose, which is used to initialize intensity-based image registration to refine the current pose of the bronchoscope camera. We evaluate the KLT-, SURF-, and SIFT-based bronchoscope camera motion tracking methods on patient datasets. According to experimental results, we may conclude that SIFT features are more robust than KLT and SURF features at predicting the bronchoscope motion, and all methods for predicting the bronchoscope camera motion show a significant performance boost compared to sole intensity-based image registration without an additional position sensor.
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References
Bauer, J., Süunderhauf, N., Protzel, P.: Comparing several implementations of two recently published feature detectors. In: Proc. of the International Conference on Intelligent and Autonomous Systems (2007)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Cuevas, E., Zaldivar, D., Rojas, R.: Kalman filter for vision tracking. Tech. Rep. B 05-12, Freie University Berlin (October 2005)
Burschka, D., Li, M., Ishiia, M., Taylora, R.H., Hager, G.D.: Scale-invariant registration of monocular endoscopic images to CT-scans for sinus surgery. Medical Image Analysis 9(5), 413–426 (2005)
Deguchi, D., Mori, K., Feuerstein, M., Kitasaka, T., Maurer Jr., C.R., Suenaga, Y., Takabatake, H., Mori, M., Natori, H.: Selective image similarity measure for bronchoscope tracking based on image registration. Medical Image Analysis 13(4), 621–633 (2009)
Deligianni, F., Chung, A.J., Yang, G.Z.: Nonrigid 2-D/3-D registration for patient specific bronchoscopy simulation with statistical shape modeling: Phantom validation. IEEE Transactions on Medical Imaging 25(11), 1462–1471 (2006)
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Mori, K., Deguchi, D., Sugiyama, J., Suenaga, Y., Toriwaki, J., Maurer Jr., C.R., Takabatake, H., Natori, H.: Tracking of a bronchoscope using epipolar geometry analysis and intensity based image registration of real and virtual endoscopic images. Medical Image Analysis 6, 321–336 (2002)
Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)
Solomon, S.B., White Jr., P., Wiener, C.M., Orens, J.B., Wang, K.P.: Three-dimensionsal CT-guided bronchoscopy with a real-time electromagnetic position sensor: a comparison of two image registration methods. Chest 118(6), 1783–1787 (2000)
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Luó, X., Feuerstein, M., Reichl, T., Kitasaka, T., Mori, K. (2010). An Application Driven Comparison of Several Feature Extraction Algorithms in Bronchoscope Tracking During Navigated Bronchoscopy. In: Liao, H., Edwards, P.J."., Pan, X., Fan, Y., Yang, GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15699-1_50
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DOI: https://doi.org/10.1007/978-3-642-15699-1_50
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