Image Processing Reference
In-Depth Information
37.
Peterson, K.S., Hansen, L.K., Kolenda, T., Rostrup, E., and Strother, S.C. (2000).
On the independent components of functional neuroimages. in Proc. Inter. Conf.
on ICA and BSS . Hesinkin, Finland, 615-620.
38.
Calhoun, V. D., Adali, T., Pearlson, G. D., and Pekar, J. J. (2001). Spatial and
temporal independent component analysis of functional MRI data containing
a pair of task-related waveforms. Human Brain Mapping 13: 43-53.
39
Stone, J.V., Porrill, J., Buchel, C., and Friston, K. (1999). Spatial, temporal, and
spatiotemporal independent component analysis of fMRI data. in 18th Leeds
Statistical Research Workshop on Spatial-Temporal Modelling and its Applica-
tions . University of Leeds: July.
40.
Formisano, E., Esposito, F., Kriegeskorte, N., Tedeschi, G., Di Salle, F., and
Goebel, R. (2002). Spatial independent component analysis of functional mag-
netic resonance imaging time-series: characterization of the cortical compo-
nents. Neurocomputing . 49: 241-254.
41.
Beckmann, A. and Smith, S.M. (2004). Probabilistic independent component
analysis for functional magnetic resonance imaging . IEEE Trans. Med. Imaging .
23(2): 137-152.
42.
Nakada, T., Suzuki, K., Fujii, Y., Matsuzawa, H., and Kwee, I.L. (2000). Inde-
pendent component-cross correlation-sequential epoch (ICS) analysis of high field
fMRI time series: direct visualization of dual representation of the primary motor
cortex in human. Neurosci. Res . 37: 237-244.
43.
Calhoun, V., Adali, T., and Pearlson, G. (2001). Independent components analysis
applied to fMRI data: A natural model and order selection. in Proc. NSIP , Baltimore.
44.
Calhoun, V., Adali, T., and Pearlson, G. (2001). Independent components analysis
applied to fMRI data: a generative model for validating results. in Proc. NNSP ,
Falmouth, MA.
45.
Vanello, N., Positano, V., Ricciardi, E., Santarelli, M.F., Guazzelli, M., Pietrini,
P., and Landini, L. (2003). Separation of movement and task-related fMRI signal
changes in a simulated data set by independent component analysis. in 9th Int.
Conf. on Func. Mapp. of the Hum. Brain . New York, NY, June 19-22. Available
on CD-Rom in NeuroImage , 19(2).
46.
Esposito, F., Formisano, E., Seifritz, E., Goebel, R., Morrone, R., Tedeschi, G.,
and Di Salle, F. (2002). Spatial independent component analysis of functional
MRI time-series: to what extent do results depend on the algorithm used? Human
Brain Mapping 16: 146-157.
47.
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., and Evans, A.
C. (1996). A unified statistical approach for determining significant signals in
images of cerebral activation. Hum. Brain Mapp . 4: 58-73.
48.
Smith, S. and Brady, J. (1997). SUSAN-A new approach to low-level image
processing. Int. J. Comput. Vis. 23(1): 45-78.
49.
Somorjai, R.L., Vivanco, R., and Pizzi, N. (2002). A novel, direct spatio-temporal
approach for analyzing fMRI experiments. Art. Intell. Med. 25: 5-17.
50.
Ward, J.H., Jr. (1963). Hierarchical grouping to optimize an objective function .
J. Am. Stat. Assoc. 58: 236-244.
51.
Biswal, B., Yetkin, F.Z., Haughton, V.M., and Hyde, J.S. (1995). Functional
connectivity in the motor cortex of resting human brain using echo-planar MR
imaging. Magn. Reson. Med. 34: 537-541.
52.
Hartigan, J.A. and Wong, M.A. (1979). A k -means clustering algorithm. Appl.
Stat. 28: 100-108
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