Abstract
In any medical data analysis a good visualization of specific parts or tissues are fundamental in order to perform accurate diagnosis and treatments. For a better understanding of the data, a segmentation process of the images to isolate the area or region of interest is important to be applied beforehand any visualization step. In this paper we present a method for mandibular structure surface extraction and reconstruction from CT-data images. We tested several methods and algorithms in order to find a fast and feasible approach that could be applicable in clinical procedures, providing practical and efficient tools for mandibular structures analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1976)
Enciso, R., Memon, A., Mah, J.: 3D visualization of the craniofacial patient: Volume segmentation, data integration and animation. In: Proceedings of the Conferences on Orthodontics Advances in Science and Technology (2002)
Lobregts, S.: Dental implant surgery: planning and guidance. Medicamundi 4, 30–35 (2001)
Tognola, G., Parazzini, M., Pedretti, G., Ravazzani, P., Grandori, F., Pesatori, A., Norgia, M., Svelto, C.: Novel 3D reconstruction method for mandibular distraction planning. In: IST 2006 - In Proceedings Of International Workshop on Imaging Systems and Techniques, pp. 82–85 (2006)
Tognola, G., Parazzini, M., Pedretti, G., Ravazzani, P., Svelto, C., Norgia, M., Grandori, F.: Three-dimensional reconstruction and image processing in mandibular distraction planning. IEEE Transactions on Instrumentation and Measurement 55(6), 1959–1964 (2006)
Lamecker, H., Zachow, S., Wittmers, A., Weber, B., Hege, H.-C., Isholtz, B., Stiller, M.: Automatic segmentation of mandibles in low-dose ct-data. International Journal of Computer Assisted Radiology and Surgery 1, 393–394 (2006)
Krsek, P., Spanel, M., Krupa, P., Marek, I., Cernochov, P.: Teeth and jaw 3D reconstruction in stomatology. In: Medical Information Visualisation - BioMedical Visualisation, vol. 1, pp. 23–28. IEEE Computer Society, Los Alamitos (2007)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, New Jersey (2002)
Taubin, G., Zhang, T., Golub, G.H.: Optimal Surface Smoothing as Filter Design. In: Proceedings of the 4th European Conference on Computer Vision, vol. 1, pp. 283–292 (1996)
Hashimoto, K., Kawashima, S., Araki, M., Iwai, K., Sawada, K., Akiyama, Y.: Comparison of image performance between cone-beam computed tomography for dental use and four-row multidetector helical CT. Journal of Oral Science 48, 27–34 (2006)
Insight Toolkit(ITK), http://www.itk.org
Visualization Toolkit(VTK), http://www.vtk.org
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barandiaran, I. et al. (2009). An Automatic Segmentation and Reconstruction of Mandibular Structures from CT-Data. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-04394-9_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
Online ISBN: 978-3-642-04394-9
eBook Packages: Computer ScienceComputer Science (R0)