WIAS R-packages for imaging / neuroscience
The packages are developed at the Weierstrass Institute for Applied Analysis and Stochastics starting within the projects "A3 - Image and signal processing in medicine and biosciences" (2005-2010) and "F10 - Image and signal processing in the biomedical sciences" (2010-2014) of the DFG Research Center MATHEON. The packages implement new methodology for adaptive data processing in imaging and neurosciences.
WIAS software collection for Imaging
Package "adimpro" for R
This packages provides functions for structure adaptive smoothing of digital images. This includes I/O functions for several image formats (including RAW), which relies on ImageMagick, image analysis and processing tools.
Reference, including documentation:
J. Polzehl, K. Tabelow. Adaptive smoothing of digital images: The R package adimpro., Journal of Statistical Software 19(1), (2007).
Download: freely available from CRAN server under GPL.
The AWS for AMIRA (TM) plugin
The AWS for AMIRA (TM) plugin implements a structural adaptive smoothing procedure for two- and three- dimensional images in the visualization software AMIRA (TM). It is available in the Zuse Institute Berlin's version of the software for research purposes (http://amira.zib.de/).
WIAS software collection for Neuroscience
Package "fmri" for R
The R-package "fmri" provides functions for analyzing single run fmri data with structure adaptive smoothing procedure. This includes I/O function for ANALYZE, AFNI, or DICOM files, linear modelling with hemodynamic response functions, signal detection using Random Field Theory. Additionally, the structural adaptive segmentation method from Polzehl et al. 2010 is implemented.
K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny. Analyzing fMRI experiments with structural adaptive smoothing procedures., NeuroImage 33(1), pp. 55-62 (2006).
J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, 52(2) pp. 515--523 (2010) .
J. Polzehl, K. Tabelow. Analyzing fMRI experiments with the fmri package in R. Version 1.0 - A users guide. WIAS-Technical Report No. 10 (2006)
J. Polzehl, K. Tabelow. fmri: A package for analyzing fmri data, RNews 7(2) 13-17 (2007).
K. Tabelow, J. Polzehl. Statistical parametric maps for functional MRI experiments in R: The package fmri., Journal of Statistical Software 44(11), (2011).
Package "dti" for R
The package contains tools for the analysis of diffusion-weighted magnetic resonance imaging data (dMRI). It can be used to read dMRI data, to estimate the diffusion tensor, for the adaptive smoothing of dMRI data, the estimation of orientation density functions or its square root, the estimation of tensor mixture models, the estimation of the diffusion kurtosis model, fiber tracking, and for the two- and three-dimensional visualization of the results.
K. Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss. Diffusion Tensor Imaging: Structural adaptive smoothing, NeuroImage 39(4), pp. 1763-1773 (2008).
S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R.M. Heidemann, J. Polzehl, Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS), Med. Image Anal., 16(1) pp. 200--211 (2012)
K. Tabelow, H.U. Voss, J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, J. Neurosci. Meth., 203 pp. 200--211 (2012).
S. Becker, K. Tabelow, S. Mohammadi, N. Weiskopf, J. Polzehl, Adaptive smoothing of multi-shell diffusion-weighted magnetic resonance data by msPOAS, NeuroImage 95, pp. 90--105 (2014).
K. Tabelow, H.U. Voss, and J. Polzehl, Local estimation of the noise level in MRI using structural adaptation, Medical Image Analysis 20, pp. 76--86 (2015)
J. Polzehl, K. Tabelow. Structural adaptive smoothing in diffusion tensor imaging: The R package dti, Journal of Statistical Software 31(9), (2011).
J. Polzehl, K. Tabelow, Beyond the Gaussian model in diffussion-weighted imaging: The package dti, Journal of Statistical Software 44(12) (2011).
ACID-Toolbox for SPM
K. Tabelow, S. Mohammadi, N. Weiskopf, and J. Polzehl (2015), POAS4SPM --- A toolbox for SPM to denoise diffusion MRI data, Neuroinformatics 13 pp. 19-29 (2015).
This is a toolbox for SPM that implements structural adaptive smoothing for fMRI in SPM. Its main page can be found here. The package was re-written to fit the SPM toolbox API and is now released for donwload again.
The packages come with absolutely NO WARRANTY! It is not intended for any purpose! It is especially not intended for any clinical use, but for evaluation purpose only.
Karsten Tabelow Last modified: Thu Oct 13 11:45:00 CET 2016
Tel.: 030 20372-564
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