Abstract
This paper shows a method which in the first step realizes an automatic registration of the Tl- and T2-weighted MR knee images, in the second step locates the posterior cruciate ligament (PCL) on the Tl-weighted MR knee images and in the third step permits 3D visualization of the PCL structures. The automatic registration process of the Tl- and T2-weighted MR knee images is based on the entropy (or energy) measures of fuzziness. A method of location of PCL on Tl-weighted MR knee images has been designed on the basis of both entropy (or energy) measure of fuzziness and fuzzy C-means (FCM) algorithm with median modification. The 3D visualization of the PCL structures procedure, which used registration and location steps, has been implemented in MatLab and converted to Visualization Toolkit (VTK).
Keywords
- Membership Function
- Posterior Cruciate Ligament
- Main Axis
- Normalize Cross Correlation
- Visualization Toolkit
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2007 Springer-Verlag Berlin Heidelberg
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Zarychta, P. (2007). Posterior Cruciate Ligament — 3D Visualization. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_87
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DOI: https://doi.org/10.1007/978-3-540-75175-5_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
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