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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

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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