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  • 1
    Online Resource
    Online Resource
    Patch-wise adaptive weights smoothing (2018)
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Leibniz-Institut im Forschungsverbund Berlin e.V.
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    Keywords: Forschungsbericht
    Description / Table of Contents: Image reconstruction from noisy data has a long history of methodological development and is based on a variety of ideas. In this paper we introduce a new method called patch-wise adaptive smoothing, that extends the Propagation-Separation approach by using comparisons of local patches of image intensities to define local adaptive weighting schemes for an improved balance of reduced variability and bias in the reconstruction result. We present the implementation of the new method in an R-package aws and demonstrate its properties on a number of examples in comparison with other state-of-the art image reconstruction methods.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (19 Seiten, 28,0 MB) , Illustrationen, Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik no. 2520
    URL: https://doi.org/10.20347/WIAS.PREPRINT.2520
    DOI: 10.20347/WIAS.PREPRINT.2520
    Language: English
    Note: Literaturverzeichnis: Seite 15-17
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  • 2
    Online Resource
    Online Resource
    Simultaneous adaptive smoothing of relaxometry and quantitative magnetization transfer mapping (2017)
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik Leibniz-Institut im Forschungsverbund Berlin e.V.
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    Keywords: Forschungsbericht
    Description / Table of Contents: Attempts for in-vivo histology require a high spatial resolution that comes with the price of a decreased signal-to-noise ratio. We present a novel iterative and multi-scale smoothing method for quantitative Magnetic Resonance Imaging (MRI) data that yield proton density, apparent transverse and longitudinal relaxation, and magnetization transfer maps. The method is based on the propagation-separation approach. The adaptivity of the procedure avoids the inherent bias from blurring subtle features in the calculated maps that is common for non-adaptive smoothing approaches. The characteristics of the methods were evaluated on a high-resolution data set (500 mym isotropic) from a single subject and quantified on data from a multi-subject study. The results show that the adaptive method is able to increase the signal-to-noise ratio in the calculated quantitative maps while largely avoiding the bias that is otherwise introduced by spatially blurring values across tissue borders. As a consequence, it preserves the intensity contrast between white and gray matter and the thin cortical ribbon.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (27 Seiten, 3.889 KB) , Illustrationen, Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik no. 2432
    URL: https://doi.org/10.20347/WIAS.PREPRINT.2432
    DOI: 10.20347/WIAS.PREPRINT.2432
    Language: English
    Note: Literaturverzeichnis: Seite 22-25
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  • 3
    Online Resource
    Online Resource
    Consistency results and confidence intervals for adaptive '1-penalized estimators of the high-dimensional sparse precision matrix (2016)
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik Leibniz-Institut im Forschungsverbund Berlin e.V.
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    Keywords: Forschungsbericht
    Description / Table of Contents: In this paper we consider the adaptive '1-penalized estimators for the precision matrix in a finite-sample setting. We show consistency results and construct confidence intervals for the elements of the true precision matrix. Additionally, we analyze the bias of these confidence intervals. We apply the estimator to the estimation of functional connectivity networks in functional Magnetic Resonance data and elaborate the theoretical results in extensive simulation experiments.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (29 Seiten, 523 kB) , Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik No. 2229
    URL: https://edocs.tib.eu/files/e01fn16/86965795X.pdf
    Language: English
    Note: Literaturverzeichnis: Seite 26-27
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  • 4
    Online Resource
    Online Resource
    Modeling high resolution MRI: Statistical issues with low SNR (2015)
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik im Forschungsverbund Berlin e.V.
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    Keywords: Forschungsbericht
    Description / Table of Contents: Noise is a common issue for all Magnetic Resonance Imaging (MRI) techniques and obviously leads to variability of the estimates in any model describing the data. A number of special MR sequences as well as increasing spatial resolution in MR experiments further diminish the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from its theoretical value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating process and a characterization of the resulting distribution of the imaging data. We provide an adequate quasi-likelihood approach that employs these characteristics. We elaborate on the effects of typical data preprocessing and analyze the bias effects related to low SNR for the example of the diffusion tensor model in diffusion MRI. We then demonstrate that the problem is relevant even for data from the Human Connectome Project, one of the highest quality diffusion MRI data available so far.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (20 Seiten, 2.941 kB) , Illustrationen, Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik No. 2179
    URL: https://edocs.tib.eu/files/e01fn16/869244760.pdf
    Language: English
    Note: Literaturverzeichnis: Seite 15-18
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  • 5
    Online Resource
    Online Resource
    Improving accuracy and temporal resolution of learning curve estimation for within- and across-session analysis (2015)
    Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik im Forschungsverbund Berlin e.V.
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    Keywords: Forschungsbericht
    Description / Table of Contents: Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. In this approach, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors for single subjects as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from a shuttle-box avoidance experiment with Mongolian gerbils, our approach revealed performance changes occurring at multiple temporal scales within and across training sessions which were otherwise obscured in the conventional analysis. The proper assessment of the behavioral dynamics of learning at a high temporal resolution clarified and extended current descriptions of the process of avoidance learning. It further disambiguated the interpretation of neurophysiological signal changes recorded during training in relation to learning.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (27 Seiten, 830 kB) , Illustrationen, Diagramme
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik No. 2170
    URL: https://edocs.tib.eu/files/e01fn16/869042254.pdf
    Language: English
    Note: Literaturverzeichnis: Seite 23-25
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  • 6
    Online Resource
    Online Resource
    Adaptive smoothing of digital images : the R package adimpro (2006)
    Berlin : WIAS
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    Keywords: Forschungsbericht
    Description / Table of Contents: 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.
    Type of Medium: Online Resource
    Pages: Online-Ressource (20 S., 13,9 MB) , Ill., graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1177
    URL: http://webdoc.sub.gwdg.de/ebook/serien/e/wias/2006/2006/wias_preprints_1177.pdf
    URL: https://edocs.tib.eu/files/e01fn11/528719874.pdf
    Language: English
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden , Systemvoraussetzungen: Acrobat reader.
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  • 7
    Online Resource
    Online Resource
    Structural adaptive smoothing in diffusion tensor imaging : the R package dti (2008)
    Berlin : WIAS
    Details Availability
    Keywords: Forschungsbericht
    Description / Table of Contents: Diffusion Weighted Imaging has become and will certainly continue to be an important tool in medical research and diagnostics. Data obtained with Diffusion Weighted Imaging are characterized by a high noise level. Thus, estimation of quantities like anisotropy indices or the main diffusion direction may be significantly compromised by noise in clinical or neuroscience applications. Here, we present a new package dti for R, which provides functions for the analysis of diffusion weighted data within the diffusion tensor model. This includes smoothing by a recently proposed structural adaptive smoothing procedure based on the Propagation-Separation approach in the context of the widely used Diffusion Tensor Model. We extend the procedure and show, how a correction for Rician bias can be incorporated. We use a heteroscedastic nonlinear regression model to estimate the diffusion tensor. The smoothing procedure naturally adapts to different structures of different size and thus avoids oversmoothing edges and fine structures. We illustrate the usage and capabilities of the package through some examples
    Type of Medium: Online Resource
    Pages: Online-Ressource (26 S., 2,51 MB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1382
    URL: http://webdoc.sub.gwdg.de/ebook/serien/e/wias/2009/wias_preprints_1382.pdf
    URL: https://edocs.tib.eu/files/e01fn11/590801791.pdf
    Language: English
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden , Systemvoraussetzungen: Acrobat reader.
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  • 8
    Online Resource
    Online Resource
    Structural adaptive smoothing: principles and applications in imaging (2009)
    Berlin : WIAS
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    Keywords: Forschungsbericht
    Description / Table of Contents: Structural adaptive smoothing provides a new concept of edge-preserving non-parametric smoothing methods. In imaging it employs qualitative assumption on the underlying homogeneity structure of the image. The chapter describes the main principles of the approach and discusses applications ranging from image denoising to the analysis of functional and diffusion weighted Magnetic Resonance experiments.
    Type of Medium: Online Resource
    Pages: Online-Ressource (27 S., 3,43 MB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik im Forschungsverbund Berlin e.V. 1412
    URL: http://webdoc.sub.gwdg.de/ebook/serien/e/wias/2009/wias_preprints_1412.pdf
    URL: https://edocs.tib.eu/files/e01fn11/607342986.pdf
    DDC: 510
    Language: English
    Note: Literaturverz. S. 19 - 25 , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden , Systemvoraussetzungen: Acrobat reader.
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  • 9
    Online Resource
    Online Resource
    Structural adaptive segmentation for statistical parametric mapping (2010)
    Berlin : WIAS
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    Keywords: Forschungsbericht
    Description / Table of Contents: 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.
    Type of Medium: Online Resource
    Pages: Online-Ressource (22 S., 0,99 MB) , graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1484
    URL: http://webdoc.sub.gwdg.de/ebook/serien/e/wias/2012/wias_preprints_1484.pdf
    URL: https://edocs.tib.eu/files/e01fn11/664534708.pdf
    RVK:
    SI 304
    Language: English
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden , Systemvoraussetzungen: Acrobat reader.
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  • 10
    Online Resource
    Online Resource
    Beyond the diffusion tensor model: the package dti (2010)
    Berlin : WIAS
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    Keywords: Forschungsbericht
    Description / Table of Contents: Diffusion weighted imaging is a magnetic resonance based method to investigate tissue micro-structure especially in the human brain via water diffusion. Since the standard diffusion tensor model for the acquired data failes in large portion of the brain voxel more sophisticated models have bee developed. Here, we report on the package dti and how some of these models can be used with the package.
    Type of Medium: Online Resource
    Pages: Online-Ressource (22 S., 3,54 MB) , Ill., graph. Darst.
    Series Statement: Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1563
    URL: http://webdoc.sub.gwdg.de/ebook/serien/e/wias/2012/wias_preprints_1563.pdf
    URL: https://edocs.tib.eu/files/e01fn11/66521412X.pdf
    Language: English
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden , Systemvoraussetzungen: Acrobat reader.
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