3,940 research outputs found
BMICA-independent component analysis based on B-spline mutual information estimator
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA
Information Theoretical Estimators Toolbox
We present ITE (information theoretical estimators) a free and open source,
multi-platform, Matlab/Octave toolbox that is capable of estimating many
different variants of entropy, mutual information, divergence, association
measures, cross quantities, and kernels on distributions. Thanks to its highly
modular design, ITE supports additionally (i) the combinations of the
estimation techniques, (ii) the easy construction and embedding of novel
information theoretical estimators, and (iii) their immediate application in
information theoretical optimization problems. ITE also includes a prototype
application in a central problem class of signal processing, independent
subspace analysis and its extensions.Comment: 5 pages; ITE toolbox: https://bitbucket.org/szzoli/ite
Diffeomorphic Registration of Images with Variable Contrast Enhancement
Nonrigid image registration is widely used to estimate
tissue deformations in highly deformable anatomies. Among
the existing methods, nonparametric registration algorithms
such as optical flow, or Demons, usually have the advantage of
being fast and easy to use. Recently, a diffeomorphic version
of the Demons algorithm was proposed. This provides the
advantage of producing invertible displacement fields, which
is a necessary condition for these to be physical. However,
such methods are based on the matching of intensities and
are not suitable for registering images with different contrast
enhancement. In such cases, a registration method based on the
local phase like the Morphons has to be used. In this paper, a
diffeomorphic version of the Morphons registration method is
proposed and compared to conventional Morphons, Demons,
and diffeomorphic Demons. The method is validated in the
context of radiotherapy for lung cancer patients on several
4D respiratory-correlated CT scans of the thorax with and without
variable contrast enhancement
Probabilistic Atlas Based Segmentation Using Affine Moment Descriptors and Graph-Cuts
We show a procedure for constructing a probabilistic atlas based on affine moment descriptors. It uses a normalization procedure over the labeled atlas. The proposed linear registration is defined by closed-form expressions involving only geometric moments. This procedure applies both to atlas construction as atlas-based segmentation. We model the likelihood term for each voxel and each label using parametric or nonparametric distributions and the prior term is determined by applying the vote-rule. The probabilistic atlas is built with the variability of our linear registration. We have two segmentation strategy: a) it applies the proposed affine registration to bring the target image into the coordinate frame of the atlas or b) the probabilistic atlas is non-rigidly aligning with the target image, where the probabilistic atlas is previously aligned to the target image with our affine registration. Finally, we adopt a graph cut - Bayesian framework for implementing the atlas-based segmentation
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