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检索条件"任意字段=Conference on Visualization in Biomedical Computing"

共 893 条记录，以下是361-370 订阅

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Evaluation of electrical contacts using an X-ray CT 3D visualisation technique 27

Evaluation of electrical contacts using an X-ray CT 3D visua...

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27th International conference on Electrical Contacts, ICEC 2014

作者： Roussos, Constantinos Swingler, Jonathan School of Engineering and Physical Sciences Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9783800736249

X-Ray CT visualisation is a powerful tool for characterising the condition of electrical contacts and associated degradation mechanisms. The main advantage is the ability to acquire 3-dimensional views of samples without dismantling the component parts and thus destroying any features of interest. Material presented in this paper highlights the study underway to develop this technique to characterise electrical contacts. A brief review of the visualisation technique is given compared to other methods, highlighting the developments in the technology and comparing to mathematical surface modelling. Experimental data of a contact system is presented to demonstrate the advantages of the technique. The extent of the contacting interface is shown which includes asperities of different shapes and voids. The paper concludes by highlighting the types of questions in contact physics which can be answered with this technique and the developments necessary to get those answers. © 2014 VDE VERLAG GMBH. All rights reserved.

关键词： visualization

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Personal informatics weave your numbers

Personal informatics weave your numbers

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International conference on Contemporary computing and Informatics (IC3I)

作者： Sanjay Krishna Anbalagan George Grinstein Shweta Purushe Institute for Visualization Perception Research University of Massachusetts Lowell Lowell MA USA

There are often several interconnected complex factors affecting one's healthy behavior. visualization of a single factor, as current tools provide, is limited and makes overall analysis difficult. We describe a system that supports both the self-monitoring of vital statistics as well as the collaborative exploratory analysis of multivariate contextual and behavioral information with respective domain experts. These analyses, data and visualizations are automatically shared through the use Google Drive.

关键词： Weaving Data visualization Google Monitoring Servers Informatics biomedical monitoring

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Parcellation-independent multi-scale framework for brain network analysis

Parcellation-independent multi-scale framework for brain net...

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MICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2014

作者： Schirmer, M.D. Ball, G. Counsell, S.J. Edwards, A.D. Rueckert, D. Hajnal, J.V. Aljabar, P. Division of Imaging Sciences & Biomedical Engineering King’s College London St. Thomas’ Hospital London United Kingdom Centre for the Developing Brain King’s College London St. Thomas’ Hospital London United Kingdom BioMedIA Group Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783319111810

Structural brain connectivity can be characterised by studies employing diffusion MR, tractography and the derivation of network measures. However, in some subject populations, such as neonates, the lack of a generally accepted paradigm for how the brain should be segmented or parcellated leads to the application of a variety of atlas- and random-based parcellation methods. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences has yet to be resolved, in order to enable more meaningful intraand inter-subject comparisons. This work proposes a parcellation-independent multi-scale analysis of commonly used network measures to describe changes in the brain. As an illustration, we apply our framework to a neonatal serial diffusion MRI data set and show its potential in characterising developmental changes. Furthermore, we use the measures provided by the framework to investigate the inter-dependence between network measures and apply an hierarchical clustering algorithm to determine a subset of measures for characterising the brain. © Springer International Publishing Switzerland 2014.

关键词： Magnetic resonance imaging

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Fiber based comparison of whole brain tractographies with application to amyotrophic lateral sclerosis

Fiber based comparison of whole brain tractographies with ap...

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16th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2013

作者： Zimmerman-Moreno, Gali Ben Basha, Dafna Artzi, Moran Nefussy, Beatrice Drory, Vivian Aizenstein, Orna Greenspan, Hayit Department of Biomedical Engineering Tel Aviv University Tel Aviv69978 Israel Tel Aviv Sourasky Medical Center 6 Weizmann St. Tel Aviv64239 Israel

A method is proposed for comparison between whole brain white matter tractographies derived from Diffusion Tensor Imaging (DTI) scans. The method performs fiber based comparisons between DTI-derived parameter values sampled along the fibers. The individual tractographies and the parameters sampling are done in each brain’s native space. No non-linear registration to a common space is required. Our method for fiber based comparison is especially useful as a first exploratory step in neurologic population studies. It provides pointers to the locations affected by the pathology of interest in the study. It is fully automatic and does not make any grouping assumptions on the fibers. The results are presented on a single fiber resolution level and any sub-group or tract of interest can be examined. The validation of the method was conducted using a set of scans from an Amyotrophic Lateral Sclerosis (ALS) study and comparing the outcome to previous findings. © Springer International Publishing Switzerland 2014.

关键词： Fibers

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10th International Symposium on Visual computing, ISVC 2014

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10th International Symposium on Visual computing, ISVC 2014

ISBN: (纸本)9783319142487

The proceedings contain 79 papers. The special focus in this conference is on Computational Bioimaging, Computer Graphics and Motion and Tracking. The topics include: Learning splines for sparse tomographic reconstruction;analysis of biomedical images based on automated methods of image registration;incorporating structural information into groupwise registration;automatic photorealistic 3D inner mouth restoration from frontal images;voronoi diagrams of line segments in 3D, with application to automatic rigging;image dehazing using regularized optimization;direct estimation of dense scene flow and depth from a monocular sequence;3D deformable spatial pyramid for dense 3D motion flow of deformable object;optical flow computation in the presence of spatially-varying motion blur;statistical estimation of fluid flow;efficient object localization and segmentation in weakly labeled videos;an experimental evaluation of different features and nodal costs for horizon line detection;3D gesture analysis using a large-scale gesture database;a biological motivated multi-scale keypoint detector for local 3D descriptors;resistance-geodesic distance and its use in image processing and segmentation;compact description of the segments on the segmented digital image;commonality preserving image-set clustering based on diverse density;extracting noise-resistant skeleton on digital shapes for graph matching;a human perception based performance evaluation of image quality metrics;an interactive system for video summarization, browsing and retrieval;adaptive visualization of social media data for policy modeling and combining computational models and interactive visualization to support rational decision making.

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Dictionary based super-resolution for diffusion MRI

Dictionary based super-resolution for diffusion MRI

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MICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2014

作者： Yoldemir, Burak Bajammal, Mohammad Abugharbieh, Rafeef Biomedical Signal and Image Computing Laboratory The University of British Columbia VancouverBC Canada

ISBN: (纸本)9783319111810

Diffusion magnetic resonance imaging (dMRI) provides unique capabilities for non-invasive mapping of fiber tracts in the brain. It however suffers from relatively low spatial resolution, often leading to partial volume effects. In this paper, we propose to use a super-resolution approach based on dictionary learning for alleviating this *** the majority of existing super-resolution algorithms, our proposed solution does not entail acquiring multiple scans from the same subject which renders it practical in clinical settings and applicable to legacy data. Moreover, this approach can be used in conjunction with any diffusion model. Motivated by how functional connectivity (FC) reflects the underlying structural connectivity (SC), we quantitatively validate our results by investigating the consistency between SC and FC before and after super-resolving the data. Based on this scheme, we show that our method outperforms traditional interpolation strategies and the only existing single image super-resolution method for dMRI that is not dependent on a specific diffusion model. Qualitatively, we illustrate that fiber tracts and track-density maps reconstructed from super-resolved dMRI data reveal exquisite details beyond what is achievable with the original data. © Springer International Publishing Switzerland 2014.

关键词： Magnetic resonance imaging

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Animated geooral clusters for exploratory search in event data document collections 18

Animated geooral clusters for exploratory search in event da...

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2014 18th International conference on Information Visualisation: Visualisation, biomedical visualization, Visualisation on Built and Rural Environments and Geometric Modelling and Imaging, IV 2014

作者： Craig, Paul Seiler, Nena Roa Cervantes, Ana Delia Olvera Department of Computer Science and Software Engineering Xi'An Jiaotong-Liverpool University Suzhou China School of Computing Edinburgh Napier University Edinburgh United Kingdom Instituto de Física y Matemáticas Universidad Tecnológica de la Mixteca Huajuapan de-León Mexico

ISBN: (纸本)9781479941032

This paper presents a novel visual analytics technique developed to support exploratory search tasks for event data document collections. The technique supports discovery and exploration by clustering results and overlaying cluster summaries onto coordinated timeline and map views. Users can also explore and interact with search results by selecting clusters to filter and re-cluster the data with animation used to smooth the transition between views. The technique demonstrates a number of advantages over alternative methods for displaying and exploring geo-referenced search results and spatiooral data. Firstly, cluster summaries can be presented in a manner that makes them easy to read and scan. Listing representative events from each cluster also helps the process of discovery by preserving the diversity of results. Also, clicking on visual representations of geooral clusters provides a quick and intuitive way to navigate across space and time simultaneously. This removes the need to overload users with the display of too many event labels at any one time. The technique was evaluated with a group of nineteen users and compared with an equivalent text based exploratory search engine. © 2014 IEEE.

关键词： Search engines

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Representing Evidence from biomedical Literature for Clinical Decision Support: Challenges on Semantic computing and Biomedicine

Representing Evidence from Biomedical Literature for Clinica...

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International conference on Semantic computing

作者： William Hsu Medical Imaging Informatics Group University of California Los Angeles Los Angeles CA USA

The rate at which biomedical literature is being published is quickly outpacing our ability to effectively leverage this information for evidence-based medicine. While papers are readily searchable through databases such as Pub Med, clinicians are often left with the time-consuming task of finding, assessing, interpreting, and applying this information. Tools that structure evidence from published papers using a standardized data model and provide an intuitive query interface for exploring documented biomedical entities would be valuable in utilizing this information as part of the clinical decision making process. This talk presents efforts towards developing computational tools and a representation for modeling and relating evidence from multiple clinical trial reports for lung cancer. Challenges related to representing this information in a machine-interpretable manner, assessing study quality, and handling conflicting evidence are described. I discuss the development of two tools: 1) an annotator tool used to extract information from papers, mapping it to concepts in an ontology-based representation and 2) a visualization that summarizes information about a single paper based on information captured in the model. Using lung cancer as a driving example, I demonstrate how these tools help users apply information reported in literature towards individually tailored medicine.

关键词： biomedical imaging Ontologies Diseases Clinical trials Cancer Lungs

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Coupled intrinsic connectivity: A principled method for exploratory analysis of paired data

Coupled intrinsic connectivity: A principled method for expl...

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16th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2013

作者： Scheinost, Dustin Shen, Xilin Finn, Emily Sinha, Rajita Todd Constable, R. Papademetris, Xenophon Departments of Biomedical Engineering Diagnostic Radiology and Nuerosurgery Yale University New HavenCT United States

We present a novel voxel-based connectivity approach for paired functional magnetic resonance imaging (fMRI) data collected under two different conditions labeled the Coupled Intrinsic Connectivity Distribution (coupled-ICD). Our proposed method jointly models both conditions to incorporate additional spatial information into the connectivity metric. When presented with paired data, conventional voxel-based methods analyze each condition separately. However, nonlinearities introduced during processing can cause this approach to underestimate differences between conditions. We show that commonly used methods can underestimate functional changes and evaluate our coupled-ICD solution using a study comparing cocaine-dependent subjects and healthy controls. Our approach detected differences between paired conditions in similar brain regions as the conventional approaches while revealing additional changes. Follow-up seed-based analysis confirmed, via cross validation, connectivity differences between conditions in regions detected by coupled-ICD that were undetected using conventional methods. This approach of jointly analyzing paired connectivity data provides a new and important tool with many clinically relevant applications. © Springer International Publishing Switzerland 2014.

关键词： Magnetic resonance imaging

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Groupwise registration for correcting subject motion and eddy current distortions in diffusion MRI using a PCA based dissimilarity metric

Groupwise registration for correcting subject motion and edd...

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16th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2013

作者： Huizinga, W. Metz, C.T. Poot, D.H.J. de Groot, M. Niessen, W.J. Leemans, A. Klein, S. Department of Applied Sciences Imaging Science & Technology Delft University of Technology Delft Netherlands Department of Radiology & Medical Informatics Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Epidemiology Erasmus MC Rotterdam Netherlands Image Sciences Institute University Medical Center Utrecht Utrecht Netherlands

Before starting a diffusion-weighted MRI analysis, it is important to correct any misalignment between the diffusion-weighted images (DWIs) that was caused by subject motion and eddy current induced geometric distortions. Conventional methods adopt a pairwise registration approach, in which the non-DWI, a.k.a. the b D 0 image, is used as a reference. In this work, a groupwise affine registration framework, using a global dissimilarity metric, is proposed, which eliminates the need for selecting a reference image and which does not rely on a specific method that models the diffusion characteristics. The dissimilarity metric is based on principal component analysis (PCA) and is ideally suited for DWIs, in which the signal contrast varies drastically as a function of the applied gradient orientation. The proposed method is tested on synthetic data, with known ground-truth transformation parameters, and real diffusion MRI data, resulting in successful alignment. © Springer International Publishing Switzerland 2014.

关键词： Principal component analysis

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