Abstract
The paper presents algorithmic solutions dedicated to computer navigation system which is to assist bronchoscope positioning during transbronchial needle-aspiration biopsy. The navigation exploits principle of on-line registration of real images coming from endoscope camera and virtual ones generated on the base of computed-tomography (CT) data of a patient. When these images are similar an assumption is made that the bronchoscope and virtual camera have approximately the same position and view direction. In the paper the following computational aspects are described: correction of camera lens distortion, fast approximate estimation of endoscope ego-motion, reconstruction of bronchial tree from CT data by means of their segmentation and its centerline calculation, virtual views generation, registration of real and virtual images via maximization of their mutual information and, finally, efficient parallel and network implementation of the navigation system which is under development.
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References
Bartz, D.: Virtual Endoscopy in Research and Clinical Practise. Eurographics - Computer Graphics Forum 24(1) (2005)
Mori, K., Deguchi, D., Sugiyama, J., et al.: Tracking of a Bronchoscope Using Epipolar Geometry Analysis and Intensity-Based Image Registration of Real and Virtual Endoscopic Images. Medical Image Analysis 6(3) (2002)
Sherbondy, A.J., Kiraly, A.P., et al.: Virtual Bronchoscopic Approach for Combining 3D CT and Endoscopic Video. In: Medical Imaging 2000, Proc. SPIE (2000)
Helferty, J.P., Higgins, W.E.: Combined Endoscopic Video Tracking and Virtual 3D CT Registration for Surgical Guidance. In: Proc. IEEE ICIP, pp. II-961–964 (2002)
Nagao, J., Mori, K., et al.: Fast and Accurate Bronchoscope Tracking Using Image Registration and Motion Prediction. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 551–558. Springer, Heidelberg (2004)
Deligianni, F., Chung, A., Yang, G.-Z.: Predictive Camera Tracking for Bronchoscope Simulation with CONDensation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 910–916. Springer, Heidelberg (2005)
Bricault, I., Ferretti, G., Cinquin, P.: Registration of Real and CT-Derived Virtual Bronchoscopic Images to Assist Transbronchial Biopsy. IEEE Trans. on Medical Imaging 17(5), 703–714 (1998)
Deligianni, F., Chung, A., Yang, G.-Z.: pq-Space Based 2d/3D Registration for Endoscope Tracking. In: MICCAI 2003. LNCS, vol. 2878, pp. 311–318. Springer, Heidelberg (2003)
Twardowski, T., Zieliñski, T., Duda, K., Socha, M., Duplaga, M.: Fast estimation of broncho-fiberoscope egomotion for CT-guided transbronchial biopsy. In: IEEE Int. Conference on Image Processing ICIP-2006, Atlanta (2006)
Duda, K., Duplaga, M.: Automatic generation of a navigation path for virtual bronchoscopy. In: PAK, vol. 5bis, pp. 115–118 (2006) (in polish)
Duda, K., Zieliski, T., Socha, M., et al.: Navigation in bronchial tree based on motion estimation and mutual information. In: ICSES, Poland (2006)
Vijayan Asari, K., Kumar, S., Radhakrishnan, D.: A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation. IEEE Trans. on Medical Imaging 18(4), 345–354 (1999)
Helferty, J.P., Zhang, C., McLennan, G., Higgins, W.E.: Videoendoscopic Distortion Correction and Its Application to Virtual Guidance of Endoscopy. IEEE Trans. on Medical Imaging 20(7), 605–617 (2001)
Kiraly, A.P., Helferty, et al.: Three-Dimensional Path Planning for Virtual Bronchoscopy. IEEE Trans. on Medical Imaging 23(9), 1365–1379 (2004)
Bartz, D., Mayer, D., et al.: Hybrid segmentation and exploration of the human lungs. In: IEEE-Visualization-2003 (No. 03CH37496), pp. 177–184 (2003)
VTK – The Visualizarion ToolKit, http://www.vtk.org/
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. Comput. Graph. 21, 163–169 (1987)
Tian, T.Y., Tomasi, C., Heeger, D.J.: Comparison of Approaches to Egomotion Computation. In: IEEE Conf. CVPR, pp. 315–320 (1996)
Daniilidis, K.: Fixation simplifies 3D Motion Estimation. Computer Vision and Image Understanding 68(2), 158–169 (1997)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual information based registration of medical images: a survey. IEEE Trans. on Medical Imaging 22, 986–1004 (2003)
BLAS (Basic Linear Algebra Subprograms), software library, http://www.netlib.org/blas/
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Bułat, J., Duda, K., Socha, M., Turcza, P., Zieliński, T., Duplaga, M. (2008). Computational Tasks in Bronchoscope Navigation During Computer-Assisted Transbronchial Biopsy. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008. Lecture Notes in Computer Science, vol 5103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69389-5_21
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