Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
3072177 | NeuroImage | 2010 | 9 Pages |
Abstract
Functional Magnetic Resonance Imaging inherently involves noisy measurements and a severe multiple test problem. Smoothing is usually used to reduce the effective number of multiple comparisons and to locally integrate the signal and hence increase the signal-to-noise ratio. Here, we provide a new structural adaptive segmentation algorithm (AS) that naturally combines the signal detection with noise reduction in one procedure. Moreover, the new method is closely related to a recently proposed structural adaptive smoothing algorithm and preserves shape and spatial extent of activation areas without blurring their borders.
Keywords
Related Topics
Life Sciences
Neuroscience
Cognitive Neuroscience
Authors
Jörg Polzehl, Henning U. Voss, Karsten Tabelow,