检索结果-内蒙古大学图书馆

The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

来源：评论

学校读者我要写书评

暂无评论

Spatial bias in multi-atlas based segmentation

Spatial bias in multi-atlas based segmentation

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hongzhi Wang Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania USA

Multi-atlas segmentation has been widely applied in medical image analysis. With deformable registration, this technique realizes label transfer from pre-labeled atlases to unknown images. When deformable registration produces error, label fusion that combines results produced by multiple atlases is an effective way for reducing segmentation errors. Among the existing label fusion strategies, similarity-weighted voting strategies with spatially varying weight distributions have been particularly successful. We show that, weighted voting based label fusion produces a spatial bias that under-segments structures with convex shapes. The bias can be approximated as applying spatial convolution to the ground truth spatial label probability maps, where the convolution kernel combines the distribution of residual registration errors and the function producing similarity-based voting weights. To reduce this bias, we apply a standard spatial deconvolution to the spatial probability maps obtained from weighted voting. In a brain image segmentation experiment, we demonstrate the spatial bias and show that our technique substantially reduces this spatial bias.

关键词： image segmentation Kernel Convolution Deconvolution Hippocampus Accuracy Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI

Reconstruction of the human hippocampus in 3D from histology...

引用

IEEE International Symposium on Biomedical Imaging

作者： Daniel H. Adler Alex Yang Liu John Pluta Salmon Kadivar Sylvia Orozco Hongzhi Wang James C. Gee Brian B. Avants Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA

In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.

关键词： Magnetic resonance imaging image reconstruction Hippocampus Humans Stacking Hafnium Shape

来源：评论

学校读者我要写书评

暂无评论

"Nonparametric Local Smoothing" is not image registration

引用

BMC Research Notes 2012年第1期5卷 1-5页

作者： Rohlfing, Torsten Avants, Brian Neuroscience Program SRI International Menlo Park CA 94025 333 Ravenswood Avenue United States Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania School of Medicine Philadelphia PA 19104 United States

Background: image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing & Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081-2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map. Results: Using data obtained from its authors, we show experimentally that the method proposed by Xing & Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations. Conclusions: This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities. © 2012 Rohlfing and Avants;licensee BioMed Central Ltd.

关键词： Accuracy Correspondence image registration

来源：评论

学校读者我要写书评

暂无评论

Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging

Agreement between the white matter connectivity based on the...

引用

IEEE International Symposium on Biomedical Imaging

作者： Seung-Goo Kim Brian B. Avants Hyekyoung Lee James C. Gee Moo K. Chung Richard J Davidson Jamie L. Hanson Seth D. Pollak Department of Brain and Cognitive Sciences Seoul National University South Korea Department of Brain and Cognitive Sciences Department of Nuclear Medicine Institute of Radiation Medicine Medical Research Center Seoul National University South Korea Department of Biostatistics and Medical Informatics Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin Madison WI USA Waisman Laboratory for Brain Imaging and Behavior Department of Psychology University of Wisconsin Madison WI USA Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Psychology University of Wisconsin Madison WI USA Institute of Radiation Medicine Medical Research Center Seoul National University South Korea

We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined.

关键词： Correlation Diffusion tensor imaging Biomedical imaging Educational institutions Brain

来源：评论

学校读者我要写书评

暂无评论

Structural connectivity via the tensor-based morphometry

Structural connectivity via the tensor-based morphometry

引用

IEEE International Symposium on Biomedical Imaging

作者： Seung-Goo Kim Moo K. Chung Jamie L. Hanson Brian B. Avants James C. Gee Richard J. Davidson Seth D. Pollak Department of Brain and Cognitive Sciences Seoul National University South Korea Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin Madison WI USA University of Wisconsin System Madison WI US Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781424441273

The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.

关键词： Correlation Diffusion tensor imaging Biomedical imaging Brain modeling Neuroscience Joining processes

来源：评论

学校读者我要写书评

暂无评论

Comparison of volumetric registration algorithms for tensor-based morphometry

Comparison of volumetric registration algorithms for tensor-...

引用

IEEE International Symposium on Biomedical Imaging

作者： Julio Villalon Anand A. Joshi Natasha Lepore Caroline Brun Arthur W. Toga Paul M. Thompson Laboratory of Neuro Imaging School of Medicine University of California Los Angeles Los Angeles CA USA Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Department of Radiology Childrens Hospital Los Angeles University of Southern California Los Angeles CA USA

ISBN: (纸本)9781424441273

Nonlinear registration of brain MRI scans is often used to quantify morphological differences associated with disease or genetic factors. Recently, surface-guided fully 3D volumetric registrations have been developed that combine intensity-guided volume registrations with cortical surface constraints. In this paper, we compare one such algorithm to two popular high-dimensional volumetric registration methods: large-deformation viscous fluid registration, formulated in a Riemannian framework, and the diffeomorphic “Demons” algorithm. We performed an objective morphometric comparison, by using a large MRI dataset from 340 young adult twin subjects to examine 3D patterns of correlations in anatomical volumes. Surface-constrained volume registration gave greater effect sizes for detecting morphometric associations near the cortex, while the other two approaches gave greater effects sizes subcortically. These findings suggest novel ways to combine the advantages of multiple methods in the future.

关键词： Correlation Fluids Three dimensional displays Volume measurement Genetics Surface morphology Brain

来源：评论

学校读者我要写书评

暂无评论

3D mesh based wall thickness measurement: Identification of left ventricular hypertrophy phenotypes

3D mesh based wall thickness measurement: Identification of ...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： C. Tobon-Gomez C. Butakoff P. Yushkevich M. Huguet A.F. Frangi Center of Computational Imaging & Simulation Technologies in Biomedicine CISTIB Universitat Pompeu Fabra Barcelona Spain Penn Image Computing and Science Laboratory PICSL Department of Radiology University of Pennsylvania Philadelphia PA USA CETIR Sant Jordi Barcelona Spain Universitat Pompeu Fabra Barcelona Catalunya ES

Left ventricular hypertrophy (LVH) is a complex cardiac condition mainly identified by the thickening of the myocardial wall. Although most of the contemporary cardiac imaging modalities provide high resolution 3D images, the wall thickness (WT) is still measured within the acquired planes. This way of measurement may introduce an error as cardiac wall is not necessarily orthogonal to the plane. In this study we analyze how different approaches to measure WT can affect an automatic identification of hypertrophy. The compared approaches are: WT measured along surface normal and the one provided by a medial surface. For both approaches we evaluated their ability to identify LVH phenotypes by testing with two classifiers: Transductive Confidence Machine-k Nearest Neighbor (TCM-kNN) and Linear Discriminant Analysis (LDA). Fifty three subjects were included in this study: 18 patients with hypertrophic cardiomyopathy (HCM), 13 patients with hypertensive heart disease (HDD) and 22 sedentary subjects (CG). Medial surface based approach allowed obtaining higher classification accuracy in HDD patients, while normal based approach allowed for higher classification accuracy in HCM patients.

关键词： Three dimensional displays Cardiology Heart Thickness measurement Accuracy Computational modeling Diseases

来源：评论

学校读者我要写书评

暂无评论

Tensor-Based Morphometry of Fibrous Structures with Application to Human Brain White Matter

Tensor-Based Morphometry of Fibrous Structures with Applicat...

引用

12th International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Zhang, Hui Yushkevich, Paul A. Rueckert, Daniel Gee, James C. Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania United States Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783642042706

Tensor-based morphometry (TBM) is a powerful approach for examining shape changes in anatomy both across populations and in time. Our work extends the standard TBM for quantifying local volumetric changes to establish both rich and intuitive descriptors of shape changes in fibrous structures. It leverages the data from diffusion tensor imaging to determine local spatial configuration of fibrous structures and combines this information with spatial transformations derived from image registration to quantify fibrous structure-specific changes, such as local changes in fiber length and in thickness of fiber bundles. In this paper, we describe the theoretical framework of our approach in detail and illustrate its application to study brain white matter. Our results show that additional insights can be gained with the proposed analysis. © 2009 Springer-Verlag.

关键词： Diffusion tensor imaging

来源：评论

学校读者我要写书评

暂无评论

Ventricularwall thickness analysis in acute myocardial infarction and hypertrophic cardiomyopathy

Ventricularwall thickness analysis in acute myocardial infar...

引用

IEEE International Symposium on Biomedical Imaging

作者： Hui Sun Catalina Tobon-Gomez Sandhitsu R. Das Marina Huguet Paul A. Yushkevich Alejandro F. Frangi Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Philadelphia PA USA Center of Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Information and Communication Technologies Department University of Pompeu-Fabra Barcelona Spain CETIR Sant Jordi Networking Center on Biomedical Research (CIBER-BBN) Barcelona Spain

ISBN: (纸本)9781424439317

Ventricular wall thickness and thickening are important biomarkers that are informative about myocardial function, especially for patients suffering from acute myocardial infarction (AMI) and hypertrophic cardiomyopathy (HCM). In this paper, the medial modeling is applied to produce thickness and thickening maps on skeleton meshes, on which surface-based statistical analysis can be directly performed. As an example, the statistics derived from healthy group is used to identify regions with abnormal thickness or thickening in a new patient. Cluster-based analysis is also performed to study the prevalent patterns of thickness and thickening change for AMI and HCM patients in the dataset.

关键词： Myocardium Cardiology Heart Skeleton image segmentation Ambient intelligence Biomedical computing Thickness measurement Statistical analysis Shape

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：