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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:58806", author = "Wan Nural Jawahir Wan Yussof and Hans Burkhardt", title = "3D Anisotropic Diffusion for Liver Segmentation", 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.", volume = "3", number = "9", pages = "1728-5", }