In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and extent information needs to be combined...

Magnetic Resonance Spectroscopic Imaging (MRSI) measures relative concentrations of metabolites in vivo and can thus be used for the diagnosis of certain tumors. We introduce the program CLARET that makes MRSI accessible for clinical routine use. Instead of embarking on an error-prone quantification of metabolites that requires manual checking of the results in many voxels, the program uses pat...

Magnetic resonance spectroscopic images (MRSI) carry spatially resolved information about the in vivo metabolism, however, their evaluation is difficult. Problems arise especially from artifacts and noise, yielding non-evaluable signals in many voxels. We propose a two-step approach to the processing of MRSI. In the first step a non-linear classifier is employed in every voxel to determine whet...

By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base classifiers. We prove that the proposed algorithm exhibits a “grouping effect”, which encourages the select...

The human brain forms functional networks on all spatial scales. Modern fMRI scanners allow for resolving functional brain data in high resolution, enabling the study of large-scale networks that relate to cognitive processes. The analysis of such networks forms a cornerstone of experimental neuroscience. Due to the immense size and complexity of the underlying data sets, efficient evaluation a...

We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging. Our lensless system consists solely of a diffuser placed in front of a standard image sensor. Every point within the volumetric field-of-view projects a unique pseudorandom pattern of caustics on the sensor. By using a physical approximation and simple calibration scheme, we solve the large-scale inverse...

Skeletonization will probably become as valuable a tool for shape analysis in 3D, as it is in 2D. We present a topology preserving 3D skeletonization method which computes both surface and curve skeletons whose voxels are labelled with the D6 distance to the original background. The surface skeleton preserves all shape information, so (close to) complete recovery of the object is possible. The ...

One of the difficulties statistical machine translation (SMT) systems face are differences in word order. When translating from a language with rather fixed SVO word order, such as English, to a language where the preferred word order is dramatically different (such as the SOV order of Urdu, Hindi, Korean, ...), the system has to learn long-distance reordering of the words. Higher degree of fre...

Multivariate pattern recognition has recently gained in popularity as an alternative to univariate fMRI analyis, although the exceedingly high spatial dimensionality has proven problematic. Addressing this issue, we have explored the effectiveness of evolutionary algorithms in determining a limited number of voxels that, in combination, optimally discriminate between single volumes of fMRI. Usi...