Abstract
The cortex is the outermost thin layer of gray matter in the brain; geometric measurement of the cortex helps in understanding brain anatomy and function. In the quantitative analysis of the cortex from MR images, extracting the structure and obtaining a representation for various measurements are key steps. While manual segmentation is tedious and labor intensive, automatic, reliable and efficient segmentation and measurement of the cortex remain challenging problems due to its convoluted nature. A new approach of coupled surfaces propagation using level set methods is presented here for the problem of the segmentation and measurement of the cortex. Our method is motivated by the nearly constant thickness of the cortical mantle and takes this tight coupling as an important constraint. By evolving two embedded surfaces simultaneously, each driven by its own image-derived information while maintaining the coupling, a final representation of the cortical bounding surfaces and an automatic segmentation of the cortex are achieved. Characteristics of the cortex such as cortical surface area, surface curvature and thickness are then evaluated. The level set implementation of surface propagation offers the advantage of easy initialization, computational efficiency and the ability to capture deep folds of the sulci. Results and validation from various experiments on simulated and real 3D MR images are provided.
Chapter PDF
References
S.M.Blinkov and I.I.Glezer. The Human Brain In Figures and Tables. A Quantitative Handbook. Chapter X, pp182. Basic Books,Inc, Plenum Press, 1968.
H.E.Cline, W.E.Lorensen, R.Kikinis, and F.Jolesz. Three-dimensional segmentation of MR images of the head using probability and connectivity. J. Comput. Assist. Tomogr., 14(6):1037–1045, Nov./Dec. 1990.
L.D.Cohen and I.Cohen. Finite-Element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. Pattern Anal. Machine Intell., 15(11):1131–1147, Nov, 1993.
C.A.Davatzikos and J.Prince. An active contour model for mapping the cortex. IEEE Trans. Med. Imag., 14(1):65–80, March, 1995.
C.Davatzikos and R.N.Bryan. Using a deformable surface model to obtain a shape representation of cortex. IEEE Trans. Med. Imag., 15(6):785–795, 1996.
M.P.DoCarmo. Differential Geometry of Curves and Surfaces. Prentice-Hall, New Jersey, 1976.
D.Geman and S.Geman. Stochastic relaxation, Gibbs distribution and Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell., 6:721–741, 1984.
J.J.Koenderink and A.J.van Doorn. Surface shape and curvature scale. Image and Vision Computing, 10(8):557–565, 1992.
R.K.-S.Kwan, A.C.Evans and G.B.Pike, An Extensible MRI Simulator for Post-Processing Evaluation, Visualization in Biomedical Computing vol. 1131, pp 135–140, Springer-Verlag, 1996.
S.Lakshmanan and H.Derin. Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing. IEEE Trans. Pattern Anal. and Machine Intell., 11(8):799–810, 1989.
R.Leahy, T.Hebert and R.Lee. Applications of Markov Random Fields in medical imaging. Information Processing in Medical Imaging. pp:1–14. Wiley-Liss Inc, 1991.
W.Lorenson and H.Cline. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Proc. SIGGRAPH, 21(4):163–169, July 1987.
D.MacDonald, D.Avis and A.E.Evans. Multiple Surface Identification and Matching in Magnetic Resonance Images. Proc. SPIE 2359:160–169, 1994.
R.Malladi, J.A.Sethian and B.C.Vemuri. Shape modeling with front propagation: a level set approach, IEEE Trans. on Pattern Analysis and Machine Intelligence, 17(2):158–174, Feb, 1995.
R.Malladi, R.Kimmel, D.Actalsteinsson, G.Sapiro, V.Caselles and J.A. Sethian. A geometric approach to segmentation and analysis of 3D medical images. Proc. MMBIA, 1996.
R.T.Schultz and A.Chakraborty. Magnetic resonance image analysis. In E. Bigler (Ed.), Handbook of Human Brain Function: Neuroimaging, pp:9–51. New York: Plenum Press, 1996.
L.D.Selemon, G.Rajkowska and P.S.Goldman-Rakic. Abnormally High Neuronal Density In The Schizophremic Cortex, A Morphometric Analysis Of Prefrontal Area 9 and Occipital Area 17. Arch Gen Psychiatry, vol52:pp805–828, Oct 1995.
J.A.Sethian. Level set methods:evolving interfaces in geometry, fluid mechanics, computer vision and materials science. Cambridge University Press, 1996.
P.C.Teo, G.Sapiro and B.A.Wandell. Creating connected representations of cortical gray matter for functional MRI visualization. IEEE Trans.Med. Imag., 16(6):852–863, 1997.
X.Zeng, L.H.Staib, R.T.Schultz and J.S.Duncan. Volumetric layer segmentation using coupled surfaces propagation. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp:708–715, Santa Barbara, California, June, 1998.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, X., Staib, L.H., Schultz, R.T., Duncan, J.S. (1998). Segmentation and measurement of the cortex from 3D MR images. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056237
Download citation
DOI: https://doi.org/10.1007/BFb0056237
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65136-9
Online ISBN: 978-3-540-49563-5
eBook Packages: Springer Book Archive