Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
3D Anisotropic Diffusion for Liver Segmentation

Authors: Wan Nural Jawahir Wan Yussof, Hans Burkhardt

Abstract:

Liver segmentation is the first significant process for liver diagnosis of the Computed Tomography. It segments the liver structure from other abdominal organs. Sophisticated filtering techniques are indispensable for a proper segmentation. In this paper, we employ a 3D anisotropic diffusion as a preprocessing step. While removing image noise, this technique preserve the significant parts of the image, typically edges, lines or other details that are important for the interpretation of the image. The segmentation task is done by using thresholding with automatic threshold values selection and finally the false liver region is eliminated using 3D connected component. The result shows that by employing the 3D anisotropic filtering, better liver segmentation results could be achieved eventhough simple segmentation technique is used.

Keywords: 3D Anisotropic Diffusion, non-linear filtering, CT Liver.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073343

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1202

References:


[1] A. Fenster and B. Chiu, Evaluation of segmentation algorithms for medical imaging, Proceedings of the 2005 IEEE Engineering in Medicine and Bioloy 27th Annual Conference, 2005, pp. 7186-7189.
[2] F. Liu, B. Zhao and P. K. Kijewski, Liver segmentation for ct images using gvf snake, Medical Physics 32, 2005, pp. 3699-3705.
[3] G. Gerig, O. K¨ubler, R. Kikinis and F. A. Jolesz, Nonlinear anisotropc filtering of MRI data, IEE Trans. on Medical Imaging 12, 1992, pp. 221- 232.
[4] L. Massoptier and S. Casciaro, Fully automatic liver segmentation through graph-cut technique, Proceedings of the 29th Annual International, 007, pp. 5243-5246.
[5] P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 1990, pp. 629-639.
[6] S. J. Lim, Y. Y. Jeong and Y. S. Ho, Automatic liver segmentation for volume measurement in CT images. Journal of Visual Communication and Image Representation 17:4, 2006, pp. 860-875.
[7] T. Heimann, B. van Ginneken, M. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, A. Beck, C. Becker, R. Beichel, G. Bekes, F. Bello, G. Binnig, H. Bischof, A. Bornik, P.M.M. Cashman, Y. Chi, A. Cordova, B.M.