Submitted by mam2155 on January 7, 2014 - 10:53am
| Title | Structural adaptive segmentation for statistical parametric mapping. |
| Publication Type | Journal Article |
| Year of Publication | 2010 |
| Authors | Polzehl, Jörg, Voss Henning U., and Tabelow Karsten |
| Journal | Neuroimage |
| Volume | 52 |
| Issue | 2 |
| Pagination | 515-23 |
| Date Published | 2010 Aug 15 |
| ISSN | 1095-9572 |
| Keywords | Algorithms, 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 |
| 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. |
| DOI | 10.1016/j.neuroimage.2010.04.241 |
| Alternate Journal | Neuroimage |
| PubMed ID | 20420928 |