title: Structural adaptive segmentation for statistical parametric mapping.
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.
authors.affiliation: Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, 10117, Berlin, Germany. polzehl@wias-berlin.de
authors.forename: Jörg
authors.initials: J
authors.lastname: Polzehl
authors.forename: Henning U
authors.initials: HU
authors.lastname: Voss
authors.forename: Karsten
authors.initials: K
authors.lastname: Tabelow
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000295:instrumentation / Q000379:methods*; D003198:Computer Simulation; D019992:Databases as Topic; D006225:Hand / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008297:Male; D009043:Motor Activity / Q000502:physiology; D010465:Perception / Q000502:physiology; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic