Anwendung "Bildverarbeitung"

J. Polzehl, K. Tabelow, Chapter 4: Structural Adaptive Smoothing: Principles and Applications in Imaging, in: Mathematical Methods for Signal and Image Analysis and Representation, L. Florack, R. Duits, G. Jongbloed, M.-C. VAN Lieshout, L. Davies, eds., 41 of Computational Imaging and Vision, Springer, London et al., 2012, pp. 65--81, (chapter published).

K. Tabelow, B. Whitcher, eds., Magnetic Resonance Imaging in R, 44 of Journal of Statistical Software, American Statistical Association, 2011, 320 pages, (monograph published).

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 (2012) pp. 1142--1155.
Abstract

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both space and diffusion direction. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric and group operations, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. The position-orientation adaptive smoothing preserves the edges of the observed fine and anisotropic structures. The POAS-algorithm is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

K. Tabelow, H.U. Voss, J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, J. Neurosci. Meth., 203 (2012) pp. 200--211.
Abstract

In this paper we develop a tensor mixture model for diffusion weighted imaging data using an automatic model selection criterion for the order of tensor components in a voxel. We show that the weighted orientation distribution function for this model can be expanded into a mixture of angular central Gaussian distributions. We show properties of this model in extensive simulations and in a high angular resolution experimental data set. The results suggest that the model may improve imaging of cerebral fiber tracts. We demonstrate how inference on canonical model parameters may give rise to new clinical applications.

K. Tabelow, J.D. Clayden, P. Lafaye DE Micheaux, J. Polzehl, V.J. Schmid, B. Whitcher, Image analysis and statistical inference in neuroimaging with R, NeuroImage, 55 (2011) pp. 1686--1693.
Abstract

R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.

K. Tabelow, J. Polzehl, Statistical parametric maps for functional MRI experiments in R: The package fmri, J. Statist. Software, 44 (2011) pp. 1--21.
Abstract

The package fmri is provided for analysis of single run functional Magnetic Resonance Imaging data. It implements structural adaptive smoothing methods with signal detection for adaptive noise reduction which avoids blurring of edges of activation areas. fmri provides fmri analysis from time series modeling to signal detection and publication-ready images.

J. Bardin, J. Fins, D. Katz, J. Hersh, L. Heier, K. Tabelow, J. Dyke, D. Ballon, N. Schiff, H. Voss, Dissociations between behavioral and fMRI-based evaluations of cognitive function after brain injury, Brain, 134 (2011) pp. 769--782.
Abstract

Functional neuroimaging methods hold promise for the identification of cognitive function and communication capacity in some severely brain-injured patients who may not retain sufficient motor function to demonstrate their abilities. We studied seven severely brain-injured patients and a control group of 14 subjects using a novel hierarchical functional magnetic resonance imaging assessment utilizing mental imagery responses. Whereas the control group showed consistent and accurate (for communication) blood-oxygen-level-dependent responses without exception, the brain-injured subjects showed a wide variation in the correlation of blood-oxygen-level-dependent responses and overt behavioural responses. Specifically, the brain-injured subjects dissociated bedside and functional magnetic resonance imaging-based command following and communication capabilities. These observations reveal significant challenges in developing validated functional magnetic resonance imaging-based methods for clinical use and raise interesting questions about underlying brain function assayed using these methods in brain-injured subjects.

J. Polzehl, K. Tabelow, Beyond the Gaussian model in diffussion-weighted imaging: The package dti, J. Statist. Software, 44 (2011) pp. 1--26.
Abstract

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.

E. Diederichs, A. Juditsky, V. Spokoiny, Ch. Schütte, Sparse non-Gaussian component analysis, IEEE Trans. Inform. Theory, 56 (2010) pp. 3033--3047.

J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, 52 (2010) pp. 515--523.
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.

K. Tabelow, V. Piëch, J. Polzehl, H.U. Voss, High-resolution fMRI: Overcoming the signal-to-noise problem, J. Neurosci. Meth., 178 (2009) pp. 357--365.
Abstract

Increasing the spatial resolution in functional Magnetic Resonance Imaging (fMRI) inherently lowers the signal-to-noise ratio (SNR). In order to still detect functionally significant activations in high-resolution images, spatial smoothing of the data is required. However, conventional non-adaptive smoothing comes with a reduced effective resolution, foiling the benefit of the higher acquisition resolution. We show how our recently proposed structural adaptive smoothing procedure for functional MRI data can improve signal detection of high-resolution fMRI experiments regardless of the lower SNR. The procedure is evaluated on human visual and sensory-motor mapping experiments. In these applications, the higher resolution could be fully utilized and high-resolution experiments were outperforming normal resolution experiments by means of both statistical significance and information content.

J. Polzehl, K. Tabelow, Structural adaptive smoothing in diffusion tensor imaging: The R package dti, J. Statist. Software, 31 (2009) pp. 1--24.
Abstract

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.

K. Tabelow, J. Polzehl, A.M. Uluğ, J.P. Dyke, R. Watts, L.A. Heier, H.U. Voss, Accurate localization of brain activity in presurgical fMRI by structure adaptive smoothing, IEEE Trans. Med. Imaging, 27 (2008) pp. 531--537.
Abstract

An important problem of the analysis of fMRI experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, the Propagation-Separation approach (PS), a structure adaptive smoothing method, has been proposed recently. PS adapts to different shapes of activation areas by generating a spatial structure corresponding to similarities and differences between time series in adjacent locations. In this paper we demonstrate how this method results in more accurate localization of brain activity. First, it is shown in numerical simulations that PS is superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and and results in less false detections. Second, in a study of 37 presurgical planning cases we found that PS and Gaussian smoothing often yield different results, and we present examples showing aspects of the superiority of PS as applied to presurgical planning.

K. Tabelow, J. Polzehl, V. Spokoiny, H.U. Voss, Diffusion tensor imaging: Structural adaptive smoothing, NeuroImage, 39 (2008) pp. 1763--1773.
Abstract

Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the Propagation-Separation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking.

D. Divine, J. Polzehl, F. Godtliebsen, A propagation-separation approach to estimate the autocorrelation in a time-series, Nonlin. Processes Geophys., 15 (2008) pp. 591--599.

V. Katkovnik, V. Spokoiny, Spatially adaptive estimation via fitted local likelihood techniques, IEEE Trans. Signal Process., 56 (2008) pp. 873--886.
Abstract

This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modelling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood in which the signal can be well approximated by a constant. The fitted local likelihood statistics is used for selection of an adaptive size of this neighborhood. The algorithm is developed for quite a general class of observations subject to the exponential distribution. The estimated signal can be uni- and multivariable. We demonstrate a good performance of the new algorithm for Poissonian image denoising and compare of the new method versus the intersection of confidence interval (ICI) technique that also exploits a selection of an adaptive neighborhood for estimation.

O. Minet, H. Gajewski, J.A. Griepentrog, J. Beuthan, The analysis of laser light scattering during rheumatoid arthritis by image segmentation, Laser Physics Letters, 4 (2007) pp. 604--610.

H.U. Voss, K. Tabelow, J. Polzehl, O. Tchernichovski, K. Maul, D. Salgado-Commissariat, D. Ballon, S.A. Helekar, Functional MRI of the zebra finch brain during song stimulation suggests a lateralized response topography, Proc. Natl. Acad. Sci. USA, 104 (2007) pp. 10667--10672.
Abstract

Electrophysiological and activity-dependent gene expression studies of birdsong have contributed to the understanding of the neural representation of natural sounds. However, we have limited knowledge about the overall spatial topography of song representation in the avian brain. Here, we adapt the noninvasive functional MRI method in mildly sedated zebra finches (Taeniopygia guttata) to localize and characterize song driven brain activation. Based on the blood oxygenation level-dependent signal, we observed a differential topographic responsiveness to playback of bird's own song, tutor song, conspecific song, and a pure tone as a nonsong stimulus. The bird's own song caused a stronger response than the tutor song or tone in higher auditory areas. This effect was more pronounced in the medial parts of the forebrain. We found left-right hemispheric asymmetry in sensory responses to songs, with significant discrimination between stimuli observed only in the right hemisphere. This finding suggests that perceptual responses might be lateralized in zebra finches. In addition to establishing the feasibility of functional MRI in sedated songbirds, our results demonstrate spatial coding of song in the zebra finch forebrain, based on developmental familiarity and experience.

J. Polzehl, K. Tabelow, Adaptive smoothing of digital images: The R package adimpro, J. Statist. Software, 19 (2007) pp. 1--17.
Abstract

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.

J. Polzehl, K. Tabelow, fmri: A package for analyzing fmri data, R News, 7 (2007) pp. 13--17.

K. Tabelow, J. Polzehl, H.U. Voss, V. Spokoiny, Analyzing fMRI experiments with structural adaptive smoothing procedures, NeuroImage, 33 (2006) pp. 55--62.
Abstract

Data from functional magnetic resonance imaging (fMRI) consists of time series of brain images which are characterized by a low signal-to-noise ratio. In order to reduce noise and to improve signal detection the fMRI data is spatially smoothed. However, the common application of a Gaussian filter does this at the cost of loss of information on spatial extent and shape of the activation area. We suggest to use the propagation-separation procedures introduced by Polzehl and Spokoiny (2006) instead. We show that this significantly improves the information on the spatial extent and shape of the activation region with similar results for the noise reduction. To complete the statistical analysis, signal detection is based on thresholds defined by random field theory. Effects of ad aptive and non-adaptive smoothing are illustrated by artificial examples and an analysis of experimental data.

G. Blanchard, M. Kawanabe, M. Sugiyama, V. Spokoiny, K.-R. Müller, In search of non-Gaussian components of a high-dimensional distribution, J. Mach. Learn. Res., 7 (2006) pp. 247--282.
Abstract

Finding non-Gaussian components of high-dimensional data is an important preprocessing step for efficient information processing. This article proposes a new em linear method to identify the ``non-Gaussian subspace'' within a very general semi-parametric framework. Our proposed method, called NGCA (Non-Gaussian Component Analysis), is essentially based on the fact that we can construct a linear operator which, to any arbitrary nonlinear (smooth) function, associates a vector which belongs to the low dimensional non-Gaussian target subspace up to an estimation error. By applying this operator to a family of different nonlinear functions, one obtains a family of different vectors lying in a vicinity of the target space. As a final step, the target space itself is estimated by applying PCA to this family of vectors. We show that this procedure is consistent in the sense that the estimaton error tends to zero at a parametric rate, uniformly over the family. Numerical examples demonstrate the usefulness of our method.

H. Gajewski, J.A. Griepentrog, A descent method for the free energy of multicomponent systems, Discrete Contin. Dyn. Syst., 15 (2006) pp. 505--528.

A. Goldenshluger, V. Spokoiny, Recovering convex edges of image from noisy tomographic data, IEEE Trans. Inform. Theory, 52 (2006) pp. 1322--1334.

J. Polzehl, V. Spokoiny, Propagation-separation approach for local likelihood estimation, Probab. Theory Related Fields, 135 (2006) pp. 335--362.
Abstract

The paper presents a unified approach to local likelihood estimation for a broad class of nonparametric models, including, e.g., regression, density, Poisson and binary response models. The method extends the adaptive weights smoothing (AWS) procedure introduced by the authors [Adaptive weights smoothing with applications to image sequentation. J. R. Stat. Soc., Ser. B 62, 335-354 (2000)] in the context of image denoising. The main idea of the method is to describe a greatest possible local neighborhood of every design point in which the local parametric assumption is justified by the data. The method is especially powerful for model functions having large homogeneous regions and sharp discontinuities. The performance of the proposed procedure is illustrated by numerical examples for density estimation and classification. We also establish some remarkable theoretical non-asymptotic results on properties of the new algorithm. This includes the ``propagation'' property which particularly yields the root-$n$ consistency of the resulting estimate in the homogeneous case. We also state an ``oracle'' result which implies rate optimality of the estimate under usual smoothness conditions and a ``separation'' result which explains the sensitivity of the method to structural changes.

J. Griepentrog, On the unique solvability of a nonlocal phase separation problem for multicomponent systems, Banach Center Publ., 66 (2004) pp. 153-164.

A. Goldenshluger, V. Spokoiny, On the shape-from-moments problem and recovering edges from noisy Radon data, Probab. Theory Related Fields, 128 (2004) pp. 123--140.

J. Polzehl, V. Spokoiny, Image denoising: Pointwise adaptive approach, Ann. Statist., 31 (2003) pp. 30--57.
Abstract

A new method of pointwise adaptation has been proposed and studied in Spokoiny (1998) in context of estimation of piecewise smooth univariate functions. The present paper extends that method to estimation of bivariate grey-scale images composed of large homogeneous regions with smooth edges and observed with noise on a gridded design. The proposed estimator $, hatf(x) ,$ at a point $, x ,$ is simply the average of observations over a window $, hatU(x) ,$ selected in a data-driven way. The theoretical properties of the procedure are studied for the case of piecewise constant images. We present a nonasymptotic bound for the accuracy of estimation at a specific grid point $, x ,$ as a function of the number of pixel $n$, of the distance from the point of estimation to the closest boundary and of smoothness properties and orientation of this boundary. It is also shown that the proposed method provides a near optimal rate of estimation near edges and inside homogeneous regions. We briefly discuss algorithmic aspects and the complexity of the procedure. The numerical examples demonstrate a reasonable performance of the method and they are in agreement with the theoretical issues. An example from satellite (SAR) imaging illustrates the applicability of the method.

J. Polzehl, V. Spokoiny, Functional and dynamic Magnetic Resonance Imaging using vector adaptive weights smoothing, J. Roy. Statist. Soc. Ser. C, 50 (2001) pp. 485--501.
Abstract

We consider the problem of statistical inference for functional and dynamic Magnetic Resonance Imaging (MRI). A new approach is proposed which extends the adaptive weights smoothing (AWS) procedure from Polzehl and Spokoiny (2000) originally designed for image denoising. We demonstrate how the AWS method can be applied for time series of images, which typically occur in functional and dynamic MRI. It is shown how signal detection in functional MRI and analysis of dynamic MRI can benefit from spatially adaptive smoothing. The performance of the procedure is illustrated using real and simulated data.

J. Polzehl, V. Spokoiny, Adaptive Weights Smoothing with applications to image restoration, J. R. Stat. Soc. Ser. B Stat. Methodol., 62 (2000) pp. 335--354.
Abstract

We propose a new method of nonparametric estimation which is based on locally constant smoothing with an adaptive choice of weights for every pair of data-points. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on some simulated univariate and bivariate examples and compare it with other nonparametric methods. Finally we discuss applications of this procedure to magnetic resonance and satellite imaging.

K. Tabelow, Viele Tests --- viele Fehler, in: Besser als Mathe --- Moderne angewandte Mathematik aus dem MATHEON zum Mitmachen, K. Biermann, M. Grötschel, B. Lutz-Westphal, eds., Reihe: Populär, Vieweg+Teubner, Wiesbaden, 2010, pp. 117--120.

H. Gajewski, J.A. Griepentrog, A. Mielke, J. Beuthan, U. Zabarylo, O. Minet, Image segmentation for the investigation of scattered-light images when laser-optically diagnosing rheumatoid arthritis, in: Mathematics -- Key Technology for the Future, W. Jäger, H.-J. Krebs, eds., Springer, Heidelberg, 2008, pp. 149--161.

K. Tabelow, Assessing the structure of the brain, WIAS-Day, WIAS Berlin, February 18, 2013.

S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R.M. Heidemann, J. Polzehl, Position-orientation adaptive smoothing (POAS) at 7T dMRI, Ultra-Highfield MRI Scientific Symposium, Max Delbrück Communication Center,, Berlin, June 8, 2012.

S. Becker, Diffusion weighted imaging: Modeling and analysis beyond the diffusion tensor, Methodological Workshop: Structural Brain Connectivity: Diffusion Imaging---State of the Art and Beyond, October 30 - November 2, 2012, Humboldt-Universität zu Berlin, November 2, 2012.

S. Becker, Image processing via orientation scores, Workshop ``Computational Inverse Problems'', October 23 - 26, 2012, Mathematisches Forschungsinstitut Oberwolfach, October 25, 2012.

S. Becker, Revisiting: Propagation-separation approach for local likelihood estimation, PreMoLab: Moscow-Berlin-Stochastic and Predictive Modeling, May 29 - June 1, 2012, Russian Academy od Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moskau, Russische Föderation, May 31, 2012.

K. Tabelow, Adaptive methods for noise reduction in diffusion weighted MRI -- Position orientation adaptive smoothing (POAS), University College London, Wellcome Trust Centre for Neuroimaging, UK, November 1, 2012.

K. Tabelow, Functional magnetic resonance imaging: Estimation and signal detection, PreMoLab: Moscow-Berlin Stochastic and Predictive Modeling, May 31 - June 1, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, May 31, 2012.

K. Tabelow, Position-orientation adaptive smoothing (POAS) diffusion weighted imaging data, Workshop on Neurogeometry, November 15 - 17, 2012, Masaryk University, Department of Mathematics and Statistics, Brno, Czech Republic, November 16, 2012.

J. Polzehl, Adaptive methods for noise reduction in diffusion weighted MR, BRIC Seminar Series, University of North Carolina, School of Medicine, Chapel Hill, NC, USA, July 10, 2012.

J. Polzehl, Medical image analysis in R (tutorial), The 8th International R User Conference (Use R!2012), June 11 - 15, 2012, Vanderbilt University, Department of Biostatics, Nashville, TN, USA, June 12, 2012.

J. Polzehl, Modeling dMRI data: An introduction from a statistical viewpoint, Workshop on Neurogeometry, November 15 - 17, 2012, Masaryk University, Department of Mathematics and Statistics, Brno, Czech Republic, November 16, 2012.

J. Polzehl, Statistical issues in diffusion weighted MR (dMRI), PreMoLab: Moscow-Berlin Stochastic and Predictive Modeling, May 31 - June 1, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, May 31, 2012.

K. Tabelow, S. Keller , S. Mohammadi, H. Kugel, J.-S. Gerdes, J. Polzehl, M. Deppe, Structural adaptive smoothing increases sensitivity of DTI to detect microstructure alterations, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26 - 30, 2011.

K. Tabelow, H. Voss, J. Polzehl , Package dti: A framework for HARDI modeling in R, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26 - 30, 2011.

K. Tabelow, H. Voss, J. Polzehl , Structural adaptive smoothing methods for fMRI and its implementation in R, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26 - 30, 2011.

K. Tabelow, B. Whitcher, J. Polzehl, Performing tasks in medical imaging with R, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26 - 30, 2011.

K. Tabelow, Diffusion weighted imaging (DTI and beyond) using dti, The R User Conference 2011, August 15 - 18, 2011, University of Warwick, Department of Statistics, Coventry, UK, August 15, 2011.

K. Tabelow, Functional MRI using fmri, The R User Conference 2011, August 15 - 18, 2011, University of Warwick, Department of Statistics, Coventry, UK, August 15, 2011.

K. Tabelow, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Cornell University, New York, Weill Medical College, USA, June 23, 2011.

K. Tabelow, Statistical parametric maps for functional MRI experiments in R: The package fmri, The R User Conference 2011, August 15 - 18, 2011, University of Warwick, Department of Statistics, Coventry, UK, August 18, 2011.

K. Tabelow, Structural adaptive smoothing fMRI and DTI data, SFB Research Center ``Mathematical Optimization and Applications in Biomedical Sciences'', Karl-Franzens-Universität Graz, Institut für Mathematik und Wissenschaftliches Rechnen, Austria, June 8, 2011.

K. Tabelow, Structural adaptive smoothing fMRI and DTI data, Maastricht University, Faculty of Psychology and Neuroscience, The Netherlands, September 28, 2011.

J. Polzehl, Statistical issues in modeling diffusion weighted magnetic resonance data, 3rd International Conference on Statistics and Probability 2011 (IMS-China), July 8 - 11, 2011, Institute of Mathematical Statistics, Xian, China, July 10, 2011.

J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Workshop on Statistics and Neuroimaging 2011, November 23 - 25, 2011, WIAS, November 24, 2011.

K. Tabelow, J.D. Clayden, P. Lafaye DE Micheaux, J. Polzehl, V.J. Schmid, B. Whitcher, Image analysis and statistical inference in NeuroImaging with R., Human Brain Mapping 2010, Barcelona, Spain, June 6 - 10, 2010.

K. Tabelow, J. Polzehl, S. Mohammadi, M. Deppe, Impact of smoothing on the interpretation of FA maps, Human Brain Mapping 2010, Barcelona, Spain, June 6 - 10, 2010.

K. Tabelow, Structural adaptive smoothing fMRI and DTI data, Workshop on Novel Reconstruction Strategies in NMR and MRI 2010, September 9 - 11, 2010, Georg-August-Universität Göttingen, Fakultät für Mathematik und Informatik, September 11, 2010.

J. Polzehl, K. Tabelow, Image and signal processing in the biomedical sciences: Diffusion-weighted imaging modeling and beyond, 1st Annual Scientific Symposium ``Ultrahigh Field Magnetic Resonance'', Max Delbrück Center, Berlin, April 16, 2010.

J. Polzehl, Medical image analysis for structural and functional MRI, The R User Conference 2010, July 20 - 23, 2010, National Institute of Standards and Technology (NIST), Gaithersburg, USA, July 20, 2010.

J. Polzehl, Statistical issues in accessing brain functionality and anatomy, The R User Conference 2010, July 20 - 23, 2010, National Institute of Standards and Technology (NIST), Gaithersburg, USA, July 22, 2010.

J. Polzehl, Statistical problems in functional and diffusion weighted magnetic resonance, Uppsala University, Dept. of Mathematics, Graduate School in Mathematics and Computing, Sweden, May 27, 2010.

J. Polzehl, Structural adaptive smoothing in neuroscience applications, Statistische Woche Nürnberg 2010, September 14 - 17, 2010, Friedrich-Alexander-Universität Erlangen-Nürnberg, Naturwissenschaftliche Fakultät, September 16, 2010.

V. Spokoiny, Local parametric estimation, October 18 - 22, 2010, École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Rennes, France.

V. Spokoiny, Semidefinite non-Gaussian component analysis, Bivariate Penalty Choice in Model Selection, Deutsches Diabetes Zentrum Düsseldorf, June 17, 2010.

K. Tabelow, J. Polzehl, H.U. Voss, Structural adaptive smoothing methods for high-resolution fMRI, 15th Annual Meeting of the Organization for Human Brain Mapping (HBM 2009), San Francisco, USA, June 18 - 22, 2009.

K. Tabelow, A3 - Image and signal processing in the biomedical sciences: diffusion weighted imaging - modeling and beyond, Center Days 2009 (DFG Research Center scshape Matheon), March 30 - April 1, 2009, Technische Universität Berlin, March 30, 2009.

K. Tabelow, Structural adaptive methods in fMRI and DTI, Biomedical Imaging Research Seminar Series, Weill Cornell Medical College, Department of Radiology & Citigroup Biomedical Imaging Center, New York, USA, June 25, 2009.

K. Tabelow, Structural adaptive methods in fMRI and DTI, Memorial Sloan-Kettering Cancer Center, New York, USA, June 25, 2009.

K. Tabelow, Structural adaptive smoothing in fMRI and DTI, Workshop on Recent Developments in fMRI Analysis Methods, Bernstein Center for Computational Neuroscience Berlin, January 23, 2009.

J. Polzehl, K. Tabelow, Structural adaptive smoothing diffusion tensor imaging data: The R-package dti, 15th Annual Meeting of the Organization for Human Brain Mapping (HBM 2009), San Francisco, USA, June 18 - 22, 2009.

N. Serdyukova, Local parametric estimation under noise misspecification in regression problem, Workshop on structure adapting methods, November 6 - 8, 2009, WIAS, November 7, 2009.

V. Spokoiny, Adaptive local parametric estimation, Université Joseph Fourier Grenoble I, Équipe de Statistique et Modélisation Stochastique, Laboratoire Jean Kuntzmann, France, February 26, 2009.

V. Spokoiny, Adaptive local parametric methods in imaging, Technische Universität Kaiserslautern, Fachbereich Mathematik, January 23, 2009.

V. Spokoiny, Modern nonparametric statistics (block lecture), October 2 - 13, 2009, École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Rennes, France.

V. Spokoiny, Modern nonparametric statistics (block lecture), October 18 - 29, 2009, Yale University, New Haven, USA.

V. Spokoiny, Modern nonparametric statistics (block lecture), January 13 - 16, 2009, École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Rennes, France.

V. Spokoiny, Parameter tuning in statistical inverse problem, European Meeting of Statisticians (EMS2009), July 20 - 22, 2009, Université Paul Sabatier, Toulouse, France, July 21, 2009.

V. Spokoiny, Saddle point model selection, Université Toulouse 1 Capitole, Toulouse School of Economics, France, November 24, 2009.

V. Spokoiny, Saddle point model selection, Workshop on structure adapting methods, November 6 - 8, 2009, WIAS, November 7, 2009.

V. Spokoiny, Sparse non-Gaussian component analysis, Workshop ``Sparse Recovery Problems in High Dimensions: Statistical Inference and Learning Theory'', March 15 - 21, 2009, Mathematisches Forschungsinstitut Oberwolfach, March 16, 2009.

K. Tabelow, A3 - Image and signal processing in medicine and biosciences, Center Days 2008 (DFG Research Center scshape Matheon), April 7 - 9, 2008, Technische Universität Berlin, April 7, 2008.

K. Tabelow, Structure adaptive smoothing medical images, 22. Treffpunkt Medizintechnik: Fortschritte in der medizinischen Bildgebung, Charité, Campus Virchow Klinikum Berlin, May 22, 2008.

K. Tabelow, Strukturadaptive Bild- und Signalverarbeitung, Workshop of scshape Matheon with Siemens AG (Health Care Sector) in cooperation with Center of Knowledge Interchange (CKI) of Technische Universität (TU) Berlin and Siemens AG, TU Berlin, July 8, 2008.

J. Polzehl, New developments in structural adaptive smoothing: Images, fMRI and DWI, University of Tromsoe, Norway, May 27, 2008.

J. Polzehl, Smoothing fMRI and DWI data using the propagation-separation approach, University of Utah, Computing and Scientific Imaging Institute, Salt Lake City, USA, September 11, 2008.

J. Polzehl, Structural adaptive smoothing in diffusion tensor imaging, Workshop on ``Locally Adaptive Filters in Signal and Image Processing'', November 24 - 26, 2008, EURANDOM, Eindhoven, The Netherlands, November 25, 2008.

J. Polzehl, Structural adaptive smoothing using the propagation-separation approach, University of Chicago, Department of Statistics, USA, September 3, 2008.

K. Tabelow, J. Polzehl, H.U. Voss, Increasing SNR in high resolution fMRI by spatially adaptive smoothing, Human Brain Mapping Conference 2007, Chicago, USA, June 10 - 14, 2007.

K. Tabelow, J. Polzehl, H.U. Voss, Reducing the number of necessary diffusion gradients by adaptive smoothing, Human Brain Mapping Conference 2007, Chicago, USA, June 10 - 14, 2007.

K. Tabelow, A3: Image and signal processing in medicine and biosciences, A-Day des sc Matheon, Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB), December 5, 2007.

K. Tabelow, Improving data quality in fMRI and DTI by structural adaptive smoothing, Cornell University, Weill Medical College, New York, USA, June 18, 2007.

K. Tabelow, Structural adaptive signal detection in fMRI and structure enhancement in DTI, International Workshop on Image Analysis in the Life Sciences, Theory and Applications, February 28 - March 2, 2007, Johannes Kepler Universität Linz, Austria, March 2, 2007.

K. Tabelow, Structural adaptive smoothing in medical imaging, Seminar ``Visualisierung und Datenanalyse'', Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB), January 30, 2007.

J. Polzehl, Propagation-separation procedures for image processing, International Workshop on Image Analysis in the Life Sciences, Theory and Applications, February 28 - March 2, 2007, Johannes Kepler Universität Linz, Austria, March 2, 2007.

J. Polzehl, Structural adaptive smoothing in imaging problems, Spring Seminar Series, University of Minnesota, School of Statistics, College of Liberal Arts, USA, May 24, 2007.

J. Polzehl, Structural adaptive smoothing procedures by propagation-separation methods, Final meeting of the DFG Priority Program 1114, November 7 - 9, 2007, Freiburg, November 7, 2007.

K. Tabelow, J. Polzehl, H.U. Voss, V. Spokoiny, Analyzing fMRI experiments with structural adaptive smoothing methods, Human Brain Mapping Conference, Florence, Italy, June 12 - 15, 2006.

K. Tabelow, J. Polzehl, V. Spokoiny, J.P. Dyke, L.A. Heier, H.U. Voss, Accurate localization of functional brain activity using structure adaptive smoothing, ISMRM 14th Scientific Meeting & Exhibition, Seattle, USA, May 10 - 14, 2006.

K. Tabelow, Analyzing fMRI experiments with structural adaptive smoothing methods, BCCN PhD Symposium 2006, June 7 - 8, 2006, Bernstein Center for Computational Neuroscience Berlin, Bad Liebenwalde, June 8, 2006.

K. Tabelow, Image and signal processing in medicine and biosciences, Evaluation Colloquium of the DFG Research Center sc Matheon, Berlin, January 24 - 25, 2006.

J. Polzehl, Structural adaptive smoothing by propagation-separation, 69th Annual Meeting of the IMS and 5th International Symposium on Probability and its Applications, July 30 - August 4, 2006, Rio de Janeiro, Brazil, July 30, 2006.

K. Tabelow, J. Polzehl, Structure adaptive smoothing procedures in medical imaging, 19. Treffpunkt Medizintechnik ``Imaging und optische Technologien für die Medizin'', Berlin, June 1, 2005.

K. Tabelow, Adaptive weights smoothing in the analysis of fMRI data, Ludwig-Maximilians-Universität München, SFB 386, December 8, 2005.

K. Tabelow, Detecting shape and borders of activation areas infMRI data, Forschungsseminar ''Mathematische Statistik'', WIAS, Berlin, November 23, 2005.

K. Tabelow, Spatially adaptive smoothing infMRI analysis, Neuroimaging Center, Cahrité, Berlin, November 10, 2005.

J. Polzehl, Adaptive smoothing by propagation-separation, Australian National University, Center of Mathematics and its Applications, Canberra, March 31, 2005.

J. Polzehl, Image reconstruction and edge enhancement by structural adaptive smoothing, 55th Session of the International Statistical Institute (ISI), April 5 - 12, 2005, Sydney, Australia, April 8, 2005.

J. Polzehl, Propagation-separation at work: Main ideas and applications, National University of Singapore, Department of Probability Theory and Statistics, March 24, 2005.

J. Polzehl, Spatially adaptive smoothing: A propagation-separation approach for imaging problems, Joint Statistical Meetings, August 7 - 11, 2005, Minneapolis, USA, August 11, 2005.

J. Polzehl, Structural adaptive smoothing by propagation-separation methods, Ludwig-Maximilians-Universität München, SFB 386, December 7, 2005.

J. Polzehl, Local likelihood modeling by structural adaptive smoothing, University of Minnesota, School of Statistics, Minneapolis, USA, September 9, 2004.

J. Polzehl, Smoothing by adaptive weights: An overview, Chalmers University of Technology, Department of Mathematical Statistics, Gothenburg, Sweden, May 11, 2004.

J. Polzehl, Structural adaptive smoothing methods, Georg-August-Universität Göttingen, Institut für Mathematische Stochastik, January 14, 2004.

J. Polzehl, Structural adaptive smoothing methods, Tandem-Workshop on Non-linear Optimization at the Crossover of Discrete Geometry and Numerical Analysis, July 15 - 16, 2004, Technische Universität Berlin, Institut für Mathematik, July 15, 2004.

J. Polzehl, Structural adaptive smoothing methods and possible applications in imaging, Charité Berlin, NeuroImaging Center, Berlin, July 1, 2004.

J. Polzehl, Structural adaptive smoothing methods for imaging problems, Annual Conference of Deutsche Mathematiker-Vereinigung (DMV), September 13 - 17, 2004, Heidelberg, September 14, 2004.

J. Polzehl, Structural adaptive smoothing methods for imaging problems, German-Israeli Binational Workshop, October 20 - 22, 2004, Ollendorff Minerva Center for Vision and Image Sciences, Technion, Haifa, Israel, October 21, 2004.

A. Hutt, J. Polzehl, Spatial adaptive signal detection in fMRT, Human Brain Mapping Conference, New York, USA, June 17 - 22, 2003.

J. Polzehl, Adaptive smoothing procedures for image processing, Workshop on Nonlinear Analysis of Multidimensional Signals, February 25 - 28, 2003, Teistungenburg, February 25, 2003.

J. Polzehl, Image processing using Adaptive Weights Smoothing, Uppsala University, Department of Mathematics, Sweden, May 7, 2003.

J. Polzehl, Local likelihood modeling by Adaptive Weights Smoothing, Joint Statistical Meetings, August 3 - 7, 2003, San Francisco, USA, August 6, 2003.

J. Polzehl, Local modeling by structural adaptation, The Art of Semiparametrics, October 19 - 21, 2003, Berlin, October 20, 2003.

J. Polzehl, Structural adaptive smoothing methods and applications in imaging, Magnetic Resonance Seminar, Physikalisch-Technische Bundesanstalt, March 13, 2003.

J. Polzehl, Structural adaptation I: Pointwise adaptive smoothing and imaging, University of Tromso, Department of Mathematics, Norway, April 11, 2002.

J. Polzehl, Structural adaptation I: Varying coefficient regression modeling by adaptive weights smoothing, Workshop on Nonparametric Smoothing in Complex Statistical Models, April 27 - May 4, 2002, Ascona, Switzerland, April 30, 2002.

J. Polzehl, Structural adaptation methods in imaging, Joint Statistical Meetings 2002, August 11 - 15, 2002, New York, USA, August 12, 2002.

J. Polzehl, Structural adaptive smoothing and its applications in imaging and time series, Uppsala University, Department of Mathematics, Sweden, May 2, 2002.

J. Polzehl, Structural adaptive estimation, Bayer AG, Leverkusen, November 29, 2001.

J. Polzehl, Adaptive weights smoothing with applications in imaging, Universität Essen, Fachbereich Mathematik, Sfb 475, November 6, 2000.

J. Polzehl, Adaptive weights smoothing with applications to image denoising and signal detection, Université Catholique de Louvain-la-Neuve, Institut de Statistique, Belgien, September 29, 2000.

J. Polzehl, Functional and dynamic Magnet Resonance Imaging using adaptive weights smoothing, Workshop "`Mathematical Methods in Brain Mapping"', Université de Montréal, Centre de Recherches Mathématiques, Kanada, December 11, 2000.

J. Polzehl, Spatially adaptive procedures for signal detection in fMRI, Tagung "`Controlling Complexity for Strong Stochastic Dependencies"', September 10 - 16, 2000, Mathematisches Forschungsinstitut Oberwolfach, September 11, 2000.

J. Polzehl, Spatially adaptive smoothing techniques for signal detection in functional and dynamic Magnet Resonance Imaging, Human Brain Mapping 2000, San Antonio, Texas, USA, June 12 - 16, 2000.

J. Polzehl, Spatially adaptive smoothing techniques for signal detection in functional and dynamic Magnet Resonance Imaging, MEDICA 2000, Düsseldorf, November 22 - 25, 2000.