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检索条件"机构=Penn Image Computing and Science Laboratory"

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Estimation of image Bias Field with Sparsity Constraints

Estimation of Image Bias Field with Sparsity Constraints

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Yuanjie Zheng James C. Gee Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania School of Medicine

ISBN: (纸本)9781424469840;9781424469857

We propose a new scheme to estimate image bias field through introducing two sparsity constraints. One is that the bias-free image has concise representation with image gradients or coefficients of other image transformations. The other constraint is that model fit on the bias field should be as concise as possible. The new scheme enables adaptive specifications of the estimated bias field's smoothness, and results in extremely accurate solutions with more efficient optimization techniques, e.g. linear programming. These distinguish our approaches from many previous methods. Our techniques can be applied to intensity inhomogeneity correction of medical images, illumination and vignetting estimation of images captured by digital cameras.

关键词： images imagery (Psychotherapy) image Bias Bias Bias Statistical Biases bias(electronics) Digital cameras mode matching graph transformation Constraint VIGNETTING

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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

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

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

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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

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Ventricularwall thickness analysis in acute myocardial infarction and hypertrophic cardiomyopathy

Ventricularwall thickness analysis in acute myocardial infar...

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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

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Nonparametric Markov priors for tissue segmentation

Nonparametric Markov priors for tissue segmentation

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IEEE International Symposium on Biomedical Imaging

作者： Zhuang Song Suyash P. Awate James C. Gee Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781424420025

This paper presents a novel method to construct a probabilistic tissue prior, for Bayesian tissue segmentation, which is based on nonparametric Markov statistics of tissue intensities learned from training data. The proposed nonparametric Markov (NPM) prior is in contrast to the conventional tissue- probability-map (TPM) prior that is based on the voxel location in a common anatomical template space. Given a set of manually labeled voxels as the training set, the NPM prior is constructed by learning a fuzzy classification function that distinguishes the Markov statistics of tissue intensities in a statistical supervised-learning framework. The validation experiments in this paper compare the efficacy of the NPM prior to that of the TPM prior in producing tissue segmentations, and demonstrate the advantages of the NPM prior, qualitatively and quantitatively, over the TPM prior, especially in cortical regions.

关键词： image segmentation Statistics Probability Biomedical imaging Bayesian methods Biological tissues Training data Learning systems Magnetic resonance imaging image analysis

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Multivariate analysis of thalamo-cortical connectivity loss in TBI

Multivariate analysis of thalamo-cortical connectivity loss ...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Jeffrey Duda Brian Avants Junghoon Kim Hui Zhang Sunil Patel John Whyte James Gee Penn Image Computing and Science Laboratory University of Pennsylvania USA Junghoon Kim Albert Einstein Healthcare Network Moss Rehabilitation Research Institute USA

Diffusion tensor (DT) images quantify connectivity patterns in the brain while the T1 modality provides high-resolution images of tissue interfaces. Our objective is to use both modalities to build subject-specific, quantitative models of fiber connections in order to discover effects specific to a neural system. The health of this thalamo-cortical network is compromised by traumatic brain injury, and we hypothesize that these effects are due to a primary injury to the thalamus which results in subsequent compromise of radiating fibers. We first use a population-specific average T1 and DT template to label the thalamus and Brodmann areas (BA) 9,10 and 11 in each subject. We also build an expected connection model within this template space that is transferred to subject space in order to provide a prior restriction on probabilistic tracking performed in subject space. We evaluate the effect of traumatic brain injury on this prefrontal-thalamus network by quantifying, in 10 subjects and 8 controls, the mean diffusion and fractional anisotropy along fiber tracts, along with the mean diffusion within the thalamus and cortical regions. We contrast results gained by a template-based tract definition with those gained by performing analysis in the subject space. Both approaches reveal connectivity effects of TBI, specifically a region of reduced FA in the white matter connecting the thalamus to BA 10.

关键词： Brain modeling Brain injuries Diffusion tensor imaging Anisotropic magnetoresistance Performance analysis image analysis Pattern analysis Brain computer interfaces Computer interfaces Computer networks

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Spatial correspondence based asymmetry analysis in FMRI

Spatial correspondence based asymmetry analysis in FMRI

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IEEE International Symposium on Biomedical Imaging

作者： Sandhitsu R. Das Dawn Mechanic-Hamilton Marc Korczykowski Brian B. Avants John A. Detre James C. Gee Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA Center for Functional Neuroimaging Department of Neurology University of Pennsylvania Philadelphia PA USA

Asymmetry analysis of brain activation in functional magnetic resonance imaging (fMRI) experiments is commonly used to determine hemispheric specialization for cognitive function. Asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within specific regions known to subserve the function of interest or over the entire hemisphere. We introduce methodologies for carrying out asymmetry analysis both for region of interest (ROI) and whole brain based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of activation asymmetry during a memory encoding task in patients with refractory temporal lobe epilepsy (TLE). Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Using structure specific analysis, our asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracortical Amobarbital Testing (IAT), which is the widely accepted gold standard for determining laterality. Whole brain analysis, on the other hand, is used to locate regions in the brain with asymmetric activation in an exploratory analysis. These methods of quantifying asymmetry using spatial correspondence have the potential for generating more robust asymmetry measurements.

关键词： Magnetic analysis image analysis Magnetic resonance imaging Information analysis Encoding Temporal lobe Epilepsy Hippocampus Testing Gold

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Multivariate segmentation of brain tissues by fusion of MRI and DTI data

Multivariate segmentation of brain tissues by fusion of MRI ...

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IEEE International Symposium on Biomedical Imaging

作者： Suyash P. Awate Hui Zhang Tony J. Simon James C. Gee Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Psychiatry and Behavioral Sciences M.I.N.D. Institute University of California Sacramento CA USA

This paper proposes a method to improve brain-tissue segmentation, especially in subcortical region, by fusing the information in structural magnetic resonance (MR) images and diffusion tensor (DT) images in a sound statistical framework. The proposed method incorporates the information in DT images by parameterizing the space of diffusion tensors, in a principled and efficient manner, based on a set of independent orthogonal invariants. The proposed method couples the Markov tissue statistics of the structural-MR intensities with the tissue statistics of the DT invariants to define multivarite/joint probability density functions (PDFs) that differentiate brain tissues. The paper shows that while the information in DT images can allow improved differentiation between tissues in the subcortical region, which comprises anatomical structures having smooth (blob-like) shapes, it can produce unreliable results in the cortical regions that depict convoluted sulci/gyri. The proposed method exploits these characteristics of the images by introducing an appropriate anisotropic distance metric in the multivariate feature space.

关键词： Magnetic resonance imaging Diffusion tensor imaging image segmentation Tensile stress Statistics Magnetic resonance Joints Probability density function Anatomical structure Shape

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Robust regularization for the estimation of intra-voxel axon fiber orientations

Robust regularization for the estimation of intra-voxel axon...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Alonso Ramirez-Manzanares Hui Zhang Mariano Rivera James C. Gee Department of Radiology Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Facultad de Matematicas Universidad de Guanajuato Philadelphia Guanajuato Mexico Centro de Investigacion en Matematicas A.C. Guanajuato Mexico

Diffusion weighted magnetic resonance images allows one to infer white matter axon fiber orientations. Nowadays it is possible to estimate intra-voxel orientations at voxels where fibers cross or split. Though, the recovered orientations could be prone to error because of low signal to noise ratio, the complex fiber structure and/or reduced number of measurements. Spatial regularization can improve the estimations but it must be done carefully such that real information is not removed and false orientations are not introduced. In this work we propose a robust method for regularizing local multi-fiber estimations given by the Diffusion Basis Function method. Our method is based on a robust outlier rejection framework which integrates data while preserves important local information. Additionally, our method constrains the solution orientation-space within a voxel by using the diffusion tensor diffusivity profile as prior information. Our experiments using both in vivo and realistic synthetic data demonstrate the advantage of using the proposed approach.

关键词： Nerve fibers Signal to noise ratio Magnetic resonance imaging Tensile stress Laboratories Radiology Magnetic resonance Noise measurement Noise robustness In vivo

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Surface-based modeling of white matter fasciculi with orientation encoding

Surface-based modeling of white matter fasciculi with orient...

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IEEE International Symposium on Biomedical Imaging

作者： Hui Zhang Paul A. Yushkevich Tony J Simon James C. Gee Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Psychiatry and Behavioral Sciences M.I.N.D. Institute University of California슠Davis Sacramento CA USA

ISBN: (纸本)9781424420025

In this paper, we describe a novel technique for modeling sheet-like white matter (WM) fasciculi using continuous medial representation (cm-rep). In the cm-rep framework, the skeleton of a fasciculus is described by a parametric surface patch. This modeling scheme is particularly appropriate for sheet-like structures, because the shapes of such objects can be effectively captured by their skeletons. We show that dimensionality reduction can be achieved without much loss of spatial specificity by projecting data along the "less interesting" thickness direction onto the skeletons. We demonstrate that local fiber orientation of the modeled fasciculi can be encoded in our framework and show how this information can be leveraged for deriving and analyzing brain connectivity patterns on the skeleton themselves.

关键词： Encoding Skeleton Solid modeling Deformable models Shape Surface fitting Topology Tensile stress image coding Laboratories

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