Abstract
In this paper, we present a system for spinal cord and nerves segmentation from STIR-MRI. We propose an user interactive segmentation method for 3D images, which is extended from the 2D random walker algorithm and implemented with a slice-section strategy. After obtaining the 3D segmentation result, we build the 3D spinal cord and nerves model for each view using VTK, which is an open-source, freely available software. Then we obtain the point cloud of the spinal cord and nerves surface by registering the three surface models constructed from three STIR-MRI images of different directions. In the experimental results, we show the 3D segmentation results of spinal cord and nerves from the STIR-MRI (Short Tau Inversion Recovery - Magnetic Resonance Imaging)images in three different views, and also display the reconstructed 3D surface model.
This work is supported by the National Science Council in Taiwan under the Grant NSC100-2221-E-007-078.
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References
Grady, L.: Random walks for image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)
Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis 13(4), 543–563 (2009)
Smyth, P.P., Taylor, C.J., Adams, J.E.: Automatic measurement of vertebral shape using active shape models. Image and Vision Computing, 705–714 (1996)
Brejl, M., Sonka, M.: Object localization and border detection criteria design in edge-based image segmentation: Automated learning from examples. IEEE Trans. Med. Imag., 973–985 (2000)
Carballido-Gamio, J., Belongie, S.J., Majumdar, S.: Normalized cuts in 3-D for spinal MRI segmentation. IEEE Trans. Med. Imag. 23(1), 36–44 (2004)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Huang, S.H., Chu, Y.H., Lai, S.H., Novak, C.L.: Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI. IEEE Trans. Med. Imag. 28(10), 1595–1605 (2009)
Cheng, Y.Y., Chang, H.M., Su, H.R., Lai, S.H., Liu, K.C., Lin, C.H.: 3D Liver Segmentation and Model Reconstruction from CT Images. iCBEB, 654–657 (2012)
Su, H.R., Lai, S.H.: CT-MR Image Registration in 3D K-Space Based on Fourier Moment Matching. PSIVT (2), 299–310 (2011)
Chen, Y.L., Lee, T.Y., Chen, B.Y., Lai, S.H.: Bipartite Polar Classification for Surface Reconstruction. Computer Graphics Forum 30(7), 2003–2010 (2011)
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© 2013 Springer-Verlag Berlin Heidelberg
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Yen, C., Su, HR., Lai, SH., Liu, KC., Lee, RR. (2013). 3D Spinal Cord and Nerves Segmentation from STIR-MRI. In: Pan, JS., Yang, CN., Lin, CC. (eds) Advances in Intelligent Systems and Applications - Volume 2. Smart Innovation, Systems and Technologies, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35473-1_39
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DOI: https://doi.org/10.1007/978-3-642-35473-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35472-4
Online ISBN: 978-3-642-35473-1
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