11 Result(s)
within
Kilian M. Pohl
Health Informatics
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Chapter and Conference Paper
Joint Data Harmonization and Group Cardinality Constrained Classification
To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acqu...
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Chapter and Conference Paper
Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis
We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting...
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Chapter and Conference Paper
FLOOR: Fusing Locally Optimal Registrations
Most registration algorithms, such as Demons [1], align two scans by iteratively finding the deformation minimizing the image dissimilarity at each location and smoothing this minimum across the image domain. ...
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Chapter and Conference Paper
Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models
Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation m...
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Chapter and Conference Paper
Regional Manifold Learning for Deformable Registration of Brain MR Images
We propose a method for deformable registration based on learning the manifolds of individual brain regions. Recent publications on registration of medical images advocate the use of manifold learning in order...
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Chapter and Conference Paper
Temporal Shape Analysis via the Spectral Signature
In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes a...
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Chapter and Conference Paper
Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model
This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorit...
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Chapter and Conference Paper
Logarithm Odds Maps for Shape Representation
The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology. Here, we utilize LogOdds for a shape representation that demonstrates ...
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Chapter and Conference Paper
A Unifying Approach to Registration, Segmentation, and Intensity Correction
We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within th...
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Chapter and Conference Paper
Coupling Statistical Segmentation and PCA Shape Modeling
This paper presents a novel segmentation approach featuring shape constraints of multiple structures. A framework is developed combining statistical shape modeling with a maximum a posteriori segmentation prob...
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Chapter and Conference Paper
Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images
The paper introduces an algorithm which allows the automatic segmentation of multi channel magnetic resonance images. We extended the Expectation Maximization-Mean Field Approximation Segmenter, to include Loc...
