WIAS Preprint No. 1484, (2010)

Structural adaptive segmentation for statistical parametric mapping



Authors

  • Polzehl, Jörg
    ORCID: 0000-0001-7471-2658
  • Voss, Henning U.
  • Tabelow, Karsten
    ORCID: 0000-0003-1274-9951

2010 Mathematics Subject Classification

  • 92C55 62G10 62G08

Keywords

  • Image Enhancement, Functional Magnetic Resonance Imaging, Structural Adaptive Smoothing, Multiscale Testing

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 the borders.

Appeared in

  • NeuroImage, 52 (2010) pp. 515--523.

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