Structural adaptive segmentation for statistical parametric mapping.

TitleStructural adaptive segmentation for statistical parametric mapping.
Publication TypeJournal Article
Year of Publication2010
AuthorsPolzehl, Jörg, Voss Henning U., and Tabelow Karsten
Date Published2010 Aug 15
KeywordsAlgorithms, Artifacts, Artificial Intelligence, Brain, Brain Mapping, Computer Simulation, Databases as Topic, Hand, Humans, Linear Models, Magnetic Resonance Imaging, Male, Motor Activity, Perception, Phantoms, Imaging, Signal Processing, Computer-Assisted, Statistics as Topic

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.

Alternate JournalNeuroimage
PubMed ID20420928

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