Using anisotropic diffusion of probability maps for activity detection in block-design functional MRI
Publication
, Journal Article
Neoh, HS; Sapiro, G
Published in: IEEE International Conference on Image Processing
December 1, 2000
A new approach for improving the detection of pixels associated with neural activity in functional magnetic resonance imaging (fMRI) is presented. We propose to use anisotropic diffusion to exploit the spatial correlation between the active pixels in functional MRI. Specifically, in this paper the anisotropic diffusion flow is applied to a probability image, obtained either from t-map statistics or via Bayes rule. In general, this information diffusion technique can be incorporated into other activity detection algorithms before the active/non-active hard decision is made. Examples with simulated and real data show improvements over classical techniques.
Duke Scholars
Published In
IEEE International Conference on Image Processing
Publication Date
December 1, 2000
Volume
1
Start / End Page
621 / 624
Citation
APA
Chicago
ICMJE
MLA
NLM
Neoh, H. S., & Sapiro, G. (2000). Using anisotropic diffusion of probability maps for activity detection in block-design functional MRI. IEEE International Conference on Image Processing, 1, 621–624.
Neoh, H. S., and G. Sapiro. “Using anisotropic diffusion of probability maps for activity detection in block-design functional MRI.” IEEE International Conference on Image Processing 1 (December 1, 2000): 621–24.
Neoh HS, Sapiro G. Using anisotropic diffusion of probability maps for activity detection in block-design functional MRI. IEEE International Conference on Image Processing. 2000 Dec 1;1:621–4.
Neoh, H. S., and G. Sapiro. “Using anisotropic diffusion of probability maps for activity detection in block-design functional MRI.” IEEE International Conference on Image Processing, vol. 1, Dec. 2000, pp. 621–24.
Neoh HS, Sapiro G. Using anisotropic diffusion of probability maps for activity detection in block-design functional MRI. IEEE International Conference on Image Processing. 2000 Dec 1;1:621–624.
Published In
IEEE International Conference on Image Processing
Publication Date
December 1, 2000
Volume
1
Start / End Page
621 / 624