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DDC: 510 × Subject: propagation ×

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    Analysing fMRI experiments with the fmri package in R. version 1.0 : a user's guide
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2006) Polzehl, Jörg; Tabelow, Karsten
    This document describes the usage of the R package fmri to analyse functional Magnetic Resonance Imaging (fMRI) data with structure adaptive smoothing procedures (Propagation-Separation (PS) approach) as described in [7].
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    Structural adaptive smoothing for single-subject analysis in SPM: the aws4SPM-toolbox
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2008) Hoffmann, Devy; Tabelow, Karsten
    There exists a variety of software tools for analyzing functional Magnetic Resonance Imaging data. A very popular one is the freely available SPM package by the Functional Imaging Laboratory at the Wellcome Department of Imaging Neuroscience. In order to enhance the signal-to-noise ratio it provides the possibility to smooth the data in a pre-processing step by a Gaussian filter. However, this comes at the cost of reducing the effective resolution. In a series of recent papers it has been shown, that using a structural adaptive smoothing algorithm based on the Propagation-Separation method allows for enhanced signal detection while preserving the shape and spatial extent of the activation areas. Here, we describe our implementation of this algorithm as a toolbox for SPM.
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    Adaptive smoothing of digital images : the R package adimpro
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2006) Polzehl, Jörg; Tabelow, Karsten
    Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the Propagation-Separation approach by Polzehl and Spokoiny (2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.
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    The Propagation-Separation Approach: Consequences of model misspecification
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Becker, Saskia
    The article presents new results on the Propagation-Separation Approach by Polzehl and Spokoiny [2006]. This iterative procedure provides a unified approach for nonparametric estimation, supposing a local parametric model. The adaptivity of the estimator ensures sensitivity to structural changes. Originally, an additional memory step was included into the algorithm, where most of the theoretical properties were based on. However, in practice, a simplified version of the algorithm is used, where the memory step is omitted. Hence, we aim to justify this simplified procedure by means of a theoretical study and numerical simulations. In our previous study [Becker and Mathé, 2013], we analyzed the simplified Propagation-Separation Approach, supposing piecewise constant parameter functions with sharp discontinuities. Here, we consider the case of a misspecified model.