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Titlebook: Information Processing in Medical Imaging; 23rd International C James C. Gee,Sarang Joshi,Lilla Zöllei Conference proceedings 2013 Springer

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Matched Signal Detection on Graphs: Theory and Application to Brain Network Classificationted under different signal models. In the simplest scenario, we assume that the signal is deterministic with noise in a subspace spanned by a subset of eigenvectors of the graph Laplacian. The conventional matched subspace detection can be easily extended to this case. Furthermore, we study signals
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Exploring High-Order Functional Interactions via Structurally-Weighted LASSO Modelsul interaction patterns reflect the working mechanisms of neural systems and represent their relationships with the external world. Most current research approaches in the neuroimaging field, however, focus on pair-wise functional/effective connectivity and are thus unable to handle high-order, netw
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Feature-Based Alignment of Volumetric Multi-modal Imagestures. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a prob
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Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration simultaneously estimates the parameters controlling the smoothness of the diffeomorphic transformations. To achieve this, we introduce a Monte Carlo Expectation Maximization algorithm, where the expectation step is approximated via Hamiltonian Monte Carlo sampling on the manifold of diffeomorphisms
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Tree-Space Statistics and Approximations for Large-Scale Analysis of Anatomical Treesistical properties of leaf-labeled anatomical trees with geometric edge attributes by considering the anatomical trees as points in the geometric space of leaf-labeled trees. This tree-space is a geodesic metric space where any two trees are connected by a unique shortest path, which corresponds to
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Predicting Cognitive Data from Medical Images Using Sparse Linear Regressionetween medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly c
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A Multiple Hypothesis Based Method for Particle Tracking and Its Extension for Cell Segmentationnd subcellular processes which may lead to better understanding of some disease processes. In this paper, we present an automatic particle tracking method and apply it for analyzing an essential subcellular process, namely clathrin mediated endocytosis using total internal reflection microscopy. Par
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A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequencesa reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a rec
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