3,381 research outputs found
Geometric tree kernels: Classification of COPD from airway tree geometry
Methodological contributions: This paper introduces a family of kernels for
analyzing (anatomical) trees endowed with vector valued measurements made along
the tree. While state-of-the-art graph and tree kernels use combinatorial
tree/graph structure with discrete node and edge labels, the kernels presented
in this paper can include geometric information such as branch shape, branch
radius or other vector valued properties. In addition to being flexible in
their ability to model different types of attributes, the presented kernels are
computationally efficient and some of them can easily be computed for large
datasets (N of the order 10.000) of trees with 30-600 branches. Combining the
kernels with standard machine learning tools enables us to analyze the relation
between disease and anatomical tree structure and geometry. Experimental
results: The kernels are used to compare airway trees segmented from low-dose
CT, endowed with branch shape descriptors and airway wall area percentage
measurements made along the tree. Using kernelized hypothesis testing we show
that the geometric airway trees are significantly differently distributed in
patients with Chronic Obstructive Pulmonary Disease (COPD) than in healthy
individuals. The geometric tree kernels also give a significant increase in the
classification accuracy of COPD from geometric tree structure endowed with
airway wall thickness measurements in comparison with state-of-the-art methods,
giving further insight into the relationship between airway wall thickness and
COPD. Software: Software for computing kernels and statistical tests is
available at http://image.diku.dk/aasa/software.php.Comment: 12 page
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space
This paper proposes a novel framework for multi-group shape analysis relying
on a hierarchical graphical statistical model on shapes within a population.The
framework represents individual shapes as point setsmodulo translation,
rotation, and scale, following the notion in Kendall shape space.While
individual shapes are derived from their group shape model, each group shape
model is derived from a single population shape model. The hierarchical model
follows the natural organization of population data and the top level in the
hierarchy provides a common frame of reference for multigroup shape analysis,
e.g. classification and hypothesis testing. Unlike typical shape-modeling
approaches, the proposed model is a generative model that defines a joint
distribution of object-boundary data and the shape-model variables.
Furthermore, it naturally enforces optimal correspondences during the process
of model fitting and thereby subsumes the so-called correspondence problem. The
proposed inference scheme employs an expectation maximization (EM) algorithm
that treats the individual and group shape variables as hidden random variables
and integrates them out before estimating the parameters (population mean and
variance and the group variances). The underpinning of the EM algorithm is the
sampling of pointsets, in Kendall shape space, from their posterior
distribution, for which we exploit a highly-efficient scheme based on
Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted
hierarchical model to perform (1) hypothesis testing for comparison between
pairs of groups using permutation testing and (2) classification for image
retrieval. The paper validates the proposed framework on simulated data and
demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201
Approximated and User Steerable tSNE for Progressive Visual Analytics
Progressive Visual Analytics aims at improving the interactivity in existing
analytics techniques by means of visualization as well as interaction with
intermediate results. One key method for data analysis is dimensionality
reduction, for example, to produce 2D embeddings that can be visualized and
analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a
well-suited technique for the visualization of several high-dimensional data.
tSNE can create meaningful intermediate results but suffers from a slow
initialization that constrains its application in Progressive Visual Analytics.
We introduce a controllable tSNE approximation (A-tSNE), which trades off speed
and accuracy, to enable interactive data exploration. We offer real-time
visualization techniques, including a density-based solution and a Magic Lens
to inspect the degree of approximation. With this feedback, the user can decide
on local refinements and steer the approximation level during the analysis. We
demonstrate our technique with several datasets, in a real-world research
scenario and for the real-time analysis of high-dimensional streams to
illustrate its effectiveness for interactive data analysis
Allocentric representation in the human amygdala and ventral visual stream
The hippocampus and the entorhinal cortex are considered the main brain structures for allocentric representation of the external environment. Here, we show that the amygdala and the ventral visual stream are involved in allocentric representation. Thirty-one young men explored 35 virtual environments during high-resolution functional magnetic resonance imaging (fMRI) of the medial temporal lobe (MTL) and were subsequently tested on recall of the allocentric pattern of the objects in each environment-in other words, the positions of the objects relative to each other and to the outer perimeter. We find increasingly unique brain activation patterns associated with increasing allocentric accuracy in distinct neural populations in the perirhinal cortex, parahippocampal cortex, fusiform cortex, amygdala, hippocampus, and entorhinal cortex. In contrast to the traditional view of a hierarchical MTL network with the hippocampus at the top, we demonstrate, using recently developed graph analyses, a hierarchical allocentric MTL network without a main connector hub
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201
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