Biomedical Engineering Reference
In-Depth Information
63.
P.E. Roland and K. Zilles, Brain atlases—a new research tool, Trends Neurosci.,
vol. 17, no. 11, pp. 458-467, 1994.
64.
S. Sandor and R. Leahy, Towards automated labelling of the cerebral cortex using
a deformable atlas, in 14th International Conference, Information Processing in Med-
ical Imaging Y. Bizais, C. Barillot, and R. DiPaola, Eds., (Brest, France), pp. 127-
138, IPMI, Kluwer, Aug 1995.
65.
P. Thompson and A. Toga, A surface-based technique for warping 3-dimensional
images of the brain, IEEE Trans. Med. Imaging, vol. 15, no. 4, pp. 383-392, 1996.
66.
C. Davatzikos, Spatial normalization of 3D brain images using deformable mod-
els, J. Comput. Assist. Tomogr., vol. 20, pp. 656-65, Jul-Aug 1996.
67.
B. Fischl, M. Sereno, and A. Dale, Cortical surface-based analysis. II: inflation,
flattening, and a surface-based coordinate system, Neuroimage, vol. 9, no. 2, pp. 195-
207, 1999.
68.
A. Dale, B. Fischl, and M. Sereno, Cortical surface-based analysis. I. Segmentation
and surface reconstruction, Neuroimage, vol. 9, pp. 179-194, 1999.
69.
B. Fischl, M. Sereno, R. Tootell, and A. Dale, High-resolution intersubject aver-
aging and a coordinate system for the cortical surface, Human Brain Mapping,
vol. 8, no. 4, pp. 272-84, 1999.
70.
P.M. Thompson, R.P. Woods, M.S. Mega, and A.W. Toga, Mathematical
compu-
tational challenges in creating deformable and probabilistic atlases of the human
brain, Human Brain Mapping, vol. 9, pp. 81-92, 2000.
71.
A. Evans, D. Collins, and C. Holmes, Computational approaches to quantifying
human neuroanatomical variability, in Brain Mapping: The Methods ( J. Mazziotta
and A. Toga, Eds.), pp. 343-361, Academic Press, San Diego, 1996.
72.
J. Giedd, J. Snell, N. Lange, J. Rajapakse, B. Casey, P. Kozuch, A. Vaituzis, Y. Vauss,
S. Hamburger, D. Kaysen, and J. Rapoport, Quantitative magnetic resonance
imaging of human brain development: ages 4-18, Cereb. Cortex, vol. 6, no. 4, pp. 551-
60, 1996.
73.
A.P. Zijdenbos and A.C. Evans, Stereotaxic mapping in automatic quantification
of neuropathology: application to multiple sclerosis, in Proceed. of the Third Int.
Conf. on Functional Mapping of the Human Brain, Copenhagen, p. 396, May 1997.
74.
J.G. Sled, A.P. Zijdenbos, and A.C. Evans, A non-parametric method for automatic
correction of intensity non-uniformity in MRI data, IEEE Trans. on Med. Imaging,
vol. 17, no. 1, pp. 87-97, Feb. 1998.
75.
A.P. Zijdenbos, B.M. Dawant, and R.A. Margolin, Inter- and intra-slice intensity
correction in MRI, in Proceed. the 14th Int. Conf. Inf. Process. in Med. Imaging (IPMI),
(France), pp. 349-350, June 1995.
76.
G. Gerig, O. Kübler, R. Kikinis, and F.A. Jolesz, Nonlinear anisotropic filtering
of MRI data, IEEE Trans. on Med. Imaging, vol. 11, pp. 221-232, June 1992.
77.
D. MacDonald, D. Avis, and A.C. Evans, Multiple surface identification and
matching in magnetic resonance images, in Proceed. of the 3rd Int. Conf. Visualiza-
tion in Biomed. Computing, SPIE, vol. 2359, pp. 160-169, 1994.
78.
D. MacDonald, Identifying Geometrically Simple Surfaces from Three Dimen-
sional Data. PhD thesis, McGill University, Montreal, Canada, December 1994.
79.
A.P. Zijdenbos, A.C. Evans, F. Riahi, J. Sled, J. Chui, and V. Kollokian, Automatic
quantification of multiple sclerosis lesion volume using stereotaxic space, in Pro-
ceed. 4th Int. Conf. Visualization in Biomed. Computing, vol. 1131 of Lecture Notes in
Computer Science, pp. 439-448, Springer-Verlag, Heidelberg, Sept. 1996.
80.
G.L. Goualher, C. Barillot, and Y. Bizais, Three-dimensional segmentation and
representation of cortical sulci using active ribbons, in Int. J. Pattern Recogn. Artif.
Intell., vol. 11, no. 8, pp. 1295-1315, 1997.
Search WWH ::




Custom Search