Abstract
The main aim of this work was to perform rigid registration of Computed Tomography (CT) and scanner datasets. The surgeon applies CT and scanner datasets in computer aided surgery and performs registration in order to visualize the location of surgical instrument on screen. It is well known fact that the registration procedure is crucial for efficient computer aiding of surgery. Selected algorithm should take into account types of datasets, required accuracy and time of calculations. The algorithms are classified basing on the various criteria: e.g. precision (coarse and fine registration), types of pointset (set of pair of corresponding points – so called point-point method, unorganized sets of points – so called surface registration). The paper presents exemplary results of applying the following algorithms: Landmark Transform (point-point registration), two methods of uninitialized Iterative Closest Point type (surface registration) and a hybrid method. The evaluated factors were: distance error (mean, minimal and maximal value) and running time of algorithm. The algorithms were tested on various datasets: (1) two similar datasets from Computed Tomography (one is geometrically transformed), (2) Computed Tomography dataset and cloud of points recorded using 3D Artec Space Spider scanner. In the first case the mean error values equaled: 102.08 mm – 121.70 mm for uninitialized ICPs methods, 0.005 mm for Landmark Transform method, and 0.0003 mm for hybrid method. The slowest algorithms in our tests were ICPs methods, faster was hybrid algorithm, and the fastest was Landmark Transform method. In the second case the distance errors were evaluated in four selected points, and the smallest errors were: 23.21 mm for uninitialized ICPs method, 0.69 mm for Landmark Transform, 9.03 for hybrid method. All algorithms were relatively slow for these large datasets, the fastest was Landmark Transform. In the second part of research we analysed the Target Registration Error (TRE) for fused Computed Tomography and scanner-recorded dataset. The TRE values equaled 0.7 mm - 2.8 mm. The results of CT – scanner datasets registration highly depend on the similarity of sets, especially their overlapping, but also their resolutions and uniformities.
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References
Świątek-Najwer, E., Majak, M., Zuk, M., Popek, M., Kulas, Z., Jaworowski, J., Pietruski, P.: The new computer and fluorescence-guided system for planning and aiding oncologic treatment. In: CARS 2017-Computer Assisted Radiology and Surgery Proceedings of the 31th International Congress and Exhibition, Barcelona, 20–24 June 2017. Supplement of the International Journal of CARS (IJCARS) (in press)
Pietruski, P., Majak, M., Świątek-Najwer, E., Popek, M., Szram, D., Żuk, M., Jaworowski, J.: Accuracy of experimental mandibular osteotomy using the image-guided sagittal saw. Int. J. Oral and Maxillofac. Surg. 45(6), 793–800 (2016)
Pietruski, P., Majak, M., Świątek-Najwer, E., Popek, M., Jaworowski, J., Żuk, M., Nowakowski, F.: Image-guided bone resection as a prospective alternative to cutting templates—a preliminary study. J. Cranio-Maxillofac. Surg. 43(7), 1021–1027 (2015)
Mimics Innovation Suite. http://biomedical.materialise.com/mis
Johnson, H., McCormick, M., Ibanez, L.: The ITK Software Guide: Design and Functionality, 4th edn. Kitware Inc, New York (2015). ISBN 9781-930934-28-3
Schroeder, W., Martin, K., Lorensen, B.: The Visualization Toolkit, 4th edn. Kitware, New York (2006). ISBN 978-1-930934-19-1
Marmulla, R., Lüth, T., Mühling, J., Hassfeld, S.: Markerless laser registration in image-guided oral and maxillofacial surgery. J. Oral Maxillofac. Surg. 62(7), 845–851 (2004)
Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: Meshlab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference 2008, pp. 129–136 (2008)
Acknowledgments
The work was supported by STRATEGMED project number STRATEGMED1/233624/4/NCBR/2014: “Development of Polish complementary molecular navigation system for the surgical treatment of tumors”, financed by the National Centre for Research and Development.
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Świątek-Najwer, E., Żuk, M., Majak, M., Popek, M. (2018). The Rigid Registration of CT and Scanner Dataset for Computer Aided Surgery. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_38
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DOI: https://doi.org/10.1007/978-3-319-68195-5_38
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