Hippocampus Segmentation using a Local Prior Model on its Boundary
Segmentation techniques based on Active Contour
Models have been strongly benefited from the use of prior information
during their evolution. Shape prior information is captured from
a training set and is introduced in the optimization procedure to
restrict the evolution into allowable shapes. In this way, the evolution
converges onto regions even with weak boundaries. Although
significant effort has been devoted on different ways of capturing
and analyzing prior information, very little thought has been devoted
on the way of combining image information with prior information.
This paper focuses on a more natural way of incorporating the
prior information in the level set framework. For proof of concept
the method is applied on hippocampus segmentation in T1-MR
images. Hippocampus segmentation is a very challenging task, due
to the multivariate surrounding region and the missing boundary
with the neighboring amygdala, whose intensities are identical. The
proposed method, mimics the human segmentation way and thus
shows enhancements in the segmentation accuracy.
[1] X. Bresson, P. Vandergheynst and J.P. Thiran, "A variational Model for
object segmentation using boundary information and shape prior driven by
the Mumford-Shah functional", International Journal of Computer Vision,
68(2), 145-162, 2006.
[2] V. Caselles, R. Kimmel, G. Sapiro, "Geodesic active contours", International
Journal of Computer Vision, 22(1), 61-79, 1997.
[3] T. Chan, W. Zhu, "Level set based shape prior segmentation", in Proc.
IEEE Conf. Comp. Vision Pattern Recognition, 1164 - 1170, vol.2, 2005.
[4] T. Chan and L. Vese, "Active contours without edges", IEEE Trans. Image
Processing, vol. 10, pp. 266277, 2001.
[5] J.G. Csernansky, L. Wang, D. Jones, D. Rastori-Cruz, J.A. Posener,
G. Heydenbrand, J.P. Miller, M.I. Miller, "Hippocampal Deformities
in Schizophrenia characterized by high dimensional brain mapping",
American Journal of Psychiatry, 159:12, 2002.
[6] C. Davatzikos, X. Tao, and D. Shen, "Hierarchical active shape models,
using the wavelet transform", IEEE Trans. on Medical Imaging, vol 22,
no 3, 2003.
[7] J. M. Leventon, E. Grimson, and O. Faugeras, "Statistical shape influence
in geodesic active contours", in Proc. IEEE Conf. Comp. Vision Pattern
Recognition, vol. 1, pp. 316323, 2000.
[8] C. Li, C. Xu, C. Gui, and M. D. Fox, "Distance Regularized Level
Set Evolution and its Application to Image Segmentation", IEEE Trans.
Image Processing, vol. 19 (12), 2010.
[9] S. Loncaric, "A survey of shape analysis techniques", Pattern Recognition,
vol.31, no 8, pp.938-1001, 1998.
[10] D.S. Marcus, T.H. Wang, J. Parker, J.G. Csernansky, J.C. Morris,
and R.L. Buckner, "Open Access Series of Imaging Studies (OASIS):
Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and
Demented Older Adults", Journal of Cognitive Neuroscience, 19, 1498-
1507.
[11] D. Martin, C. Fowlkes, and J. Malik, "Learning to detect natural image
boundaries using local brightness, color and texture cues",IEEE Trans.
on Pattern Analysis and Machine Intelligence, vol26, no 5, pp.530-549,
2004.
[12] D. Mumford, J. Shah, "Optimal approximation by piecewise smooth
function and associated variational problems", Communication on Pure
and Applied Mathematics 42, pp. 577685,1989.
[13] M.E. Shenton, G. Gerig, R.W. McCarley, G. Szekely, and R. Kikinis,
"Amygdala-hippocampal shape differences in schizophrenia: the application
of 3D shape models to volumetric MR data", Psychiatry Research
Neuroimaging, 115, 15-35, 2002.
[14] J. Talairach, P Tournoux, "Co-planar strereotaxic atlas of the human
brain: An approach to medical cerebral imaging", New York: Thieme,
1988.
[15] B. Vemuri and Y. Chen, "Joint image registration and segmentation",
Geometric level set methods in Imaging, Vision and Graphics, Springer,
pp. 251-269, 2003.
[16] J. Yang, L.H. Staib and J.S. Duncan, "Neighbor-Constrained Segmentation
with Level Set Based 3D Deformable Models", IEEE Trans. on
Medical Imaging, vol. 23(8), 2004.
[17] K. Zhang, L. Zhang, H. Song, and W. Zhou, "Active contours with
selective local or global segmentation: A new formulation and level set
method", Image Vision Computing, 28(4): 668-676, 2010.
[18] Y. Zhang, B.J. Matuszewski, L.K. Shark, C.J. Moore, "Medical image
segmentation using new hybrid level set method", IEEE Int. Conf. on
Biomedical Visualization, 2008.
[1] X. Bresson, P. Vandergheynst and J.P. Thiran, "A variational Model for
object segmentation using boundary information and shape prior driven by
the Mumford-Shah functional", International Journal of Computer Vision,
68(2), 145-162, 2006.
[2] V. Caselles, R. Kimmel, G. Sapiro, "Geodesic active contours", International
Journal of Computer Vision, 22(1), 61-79, 1997.
[3] T. Chan, W. Zhu, "Level set based shape prior segmentation", in Proc.
IEEE Conf. Comp. Vision Pattern Recognition, 1164 - 1170, vol.2, 2005.
[4] T. Chan and L. Vese, "Active contours without edges", IEEE Trans. Image
Processing, vol. 10, pp. 266277, 2001.
[5] J.G. Csernansky, L. Wang, D. Jones, D. Rastori-Cruz, J.A. Posener,
G. Heydenbrand, J.P. Miller, M.I. Miller, "Hippocampal Deformities
in Schizophrenia characterized by high dimensional brain mapping",
American Journal of Psychiatry, 159:12, 2002.
[6] C. Davatzikos, X. Tao, and D. Shen, "Hierarchical active shape models,
using the wavelet transform", IEEE Trans. on Medical Imaging, vol 22,
no 3, 2003.
[7] J. M. Leventon, E. Grimson, and O. Faugeras, "Statistical shape influence
in geodesic active contours", in Proc. IEEE Conf. Comp. Vision Pattern
Recognition, vol. 1, pp. 316323, 2000.
[8] C. Li, C. Xu, C. Gui, and M. D. Fox, "Distance Regularized Level
Set Evolution and its Application to Image Segmentation", IEEE Trans.
Image Processing, vol. 19 (12), 2010.
[9] S. Loncaric, "A survey of shape analysis techniques", Pattern Recognition,
vol.31, no 8, pp.938-1001, 1998.
[10] D.S. Marcus, T.H. Wang, J. Parker, J.G. Csernansky, J.C. Morris,
and R.L. Buckner, "Open Access Series of Imaging Studies (OASIS):
Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and
Demented Older Adults", Journal of Cognitive Neuroscience, 19, 1498-
1507.
[11] D. Martin, C. Fowlkes, and J. Malik, "Learning to detect natural image
boundaries using local brightness, color and texture cues",IEEE Trans.
on Pattern Analysis and Machine Intelligence, vol26, no 5, pp.530-549,
2004.
[12] D. Mumford, J. Shah, "Optimal approximation by piecewise smooth
function and associated variational problems", Communication on Pure
and Applied Mathematics 42, pp. 577685,1989.
[13] M.E. Shenton, G. Gerig, R.W. McCarley, G. Szekely, and R. Kikinis,
"Amygdala-hippocampal shape differences in schizophrenia: the application
of 3D shape models to volumetric MR data", Psychiatry Research
Neuroimaging, 115, 15-35, 2002.
[14] J. Talairach, P Tournoux, "Co-planar strereotaxic atlas of the human
brain: An approach to medical cerebral imaging", New York: Thieme,
1988.
[15] B. Vemuri and Y. Chen, "Joint image registration and segmentation",
Geometric level set methods in Imaging, Vision and Graphics, Springer,
pp. 251-269, 2003.
[16] J. Yang, L.H. Staib and J.S. Duncan, "Neighbor-Constrained Segmentation
with Level Set Based 3D Deformable Models", IEEE Trans. on
Medical Imaging, vol. 23(8), 2004.
[17] K. Zhang, L. Zhang, H. Song, and W. Zhou, "Active contours with
selective local or global segmentation: A new formulation and level set
method", Image Vision Computing, 28(4): 668-676, 2010.
[18] Y. Zhang, B.J. Matuszewski, L.K. Shark, C.J. Moore, "Medical image
segmentation using new hybrid level set method", IEEE Int. Conf. on
Biomedical Visualization, 2008.
@article{"International Journal of Medical, Medicine and Health Sciences:52719", author = "Dimitrios Zarpalas and Anastasios Zafeiropoulos and Petros Daras and Nicos Maglaveras", title = "Hippocampus Segmentation using a Local Prior Model on its Boundary", abstract = "Segmentation techniques based on Active Contour
Models have been strongly benefited from the use of prior information
during their evolution. Shape prior information is captured from
a training set and is introduced in the optimization procedure to
restrict the evolution into allowable shapes. In this way, the evolution
converges onto regions even with weak boundaries. Although
significant effort has been devoted on different ways of capturing
and analyzing prior information, very little thought has been devoted
on the way of combining image information with prior information.
This paper focuses on a more natural way of incorporating the
prior information in the level set framework. For proof of concept
the method is applied on hippocampus segmentation in T1-MR
images. Hippocampus segmentation is a very challenging task, due
to the multivariate surrounding region and the missing boundary
with the neighboring amygdala, whose intensities are identical. The
proposed method, mimics the human segmentation way and thus
shows enhancements in the segmentation accuracy.", keywords = "Medical imaging & processing, Brain MRI segmentation,hippocampus segmentation, hippocampus-amygdala missingboundary, weak boundary segmentation, region based segmentation,prior information, local weighting scheme in level sets, spatialdistribution of labels, gradient distribution on boundary.", volume = "5", number = "11", pages = "557-6", }
