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Structural adaptive segmentation for statistical parametric mapping

Please always quote using this URN:urn:nbn:de:0296-matheon-6853
  • 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 the borders.

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Metadaten
Author:Jörg Polzehl, Henning U. Voss, Karsten Tabelow
URN:urn:nbn:de:0296-matheon-6853
Referee:Konrad Polthier
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2010/03/23
Release Date:2010/02/23
Tag:
Institute:Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)
MSC-Classification:62-XX STATISTICS / 62Gxx Nonparametric inference / 62G05 Estimation
62-XX STATISTICS / 62Gxx Nonparametric inference / 62G08 Nonparametric regression
62-XX STATISTICS / 62Gxx Nonparametric inference / 62G10 Hypothesis testing
62-XX STATISTICS / 62Pxx Applications [See also 90-XX, 91-XX, 92-XX] / 62P10 Applications to biology and medical sciences
68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section {04 in that areag 68-00 General reference works (handbooks, dictionaries, bibliographies, etc.) / 68Uxx Computing methodologies and applications / 68U10 Image processing
92-XX BIOLOGY AND OTHER NATURAL SCIENCES / 92Cxx Physiological, cellular and medical topics / 92C55 Biomedical imaging and signal processing [See also 44A12, 65R10]
Preprint Number:693
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