Dr. Li-Yueh Hsu Profile

Dr. Li-Yueh Hsu

at National Institutes of Health

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Publications (9)

Proceedings Article | 16 March 2020 Paper

Fully automated spectral envelope and peak velocity detection from Doppler echocardiography images

Ghada Zamzmi, Li-Yueh Hsu, Wen Li, Vandana Sachdev, Sameer Antani

Proceedings Volume 11314, 113144G (2020) https://doi.org/10.1117/12.2551183

KEYWORDS: Doppler effect, Image segmentation, Doppler tomography, Electrocardiography, Image processing, Sensors, Echocardiography, Blood circulation, Heart, Image filtering

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Proceedings Article | 10 March 2011 Paper

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

Xiaoguang Lu, Hui Xue, Marie-Pierre Jolly, Christoph Guetter, Peter Kellman, Li-Yueh Hsu, Andrew Arai, Sven Zuehlsdorff, Arne Littmann, Bogdan Georgescu, Jens Guehring

Proceedings Volume 7962, 796205 (2011) https://doi.org/10.1117/12.878529

KEYWORDS: Magnetic resonance imaging, Heart, 3D modeling, Process modeling, 3D acquisition, Target detection, Databases, Detection and tracking algorithms, Functional analysis, Detector development

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Proceedings Article | 14 April 2005 Paper

Computerized measurement of myocardial infarct size on contrast-enhanced magnetic resonance images

Li-Yueh Hsu, Peter Kellman, Alex Natanzon, Glenn Hirsch, Anthony Aletras, Andrew Arai

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.595371

KEYWORDS: Magnetic resonance imaging, Image segmentation, Magnetism, Reconstruction algorithms, In vivo imaging, Image processing, 3D image processing, Tissues, Blood, Gadolinium

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Proceedings Article | 12 May 2004 Paper

Enhancement of lung nodule detection in temporal thoracic CT

Shih-Chung Lo, J. Lin, Li-Yueh Hsu, Yuan Ming Lure, Matthew Freedman, Ruiping Li, Hui Zhao

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.537621

KEYWORDS: Computed tomography, Lung, Image registration, 3D image processing, Image segmentation, Lung cancer, Cancer, 3D vision, Chest, Signal to noise ratio

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Proceedings Article | 15 May 2003 Paper

Classification of lung nodules in diagnostic CT: an approach based on 3D vascular features, nodule density distribution, and shape features

Shih-Chung Lo, Li-Yueh Hsu, Matthew Freedman, Yuan Ming Lure, Hui Zhao

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481878

KEYWORDS: Lung, Computed tomography, Image segmentation, Diagnostics, Lung cancer, Image processing, Neural networks, 3D modeling, Chest, Tumors

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Proceedings Article | 6 June 2000 Paper

Evaluation of the accuracy of an edge-based approach for multimodality brain image registration

Li-Yueh Hsu, Murray Loew

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387698

KEYWORDS: Image registration, Image segmentation, Feature extraction, Brain, Magnetic resonance imaging, Edge detection, Computed tomography, Neuroimaging, 3D image processing, Positron emission tomography

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Proceedings Article | 5 May 2000 Paper

Feature-based technique for automated image registration of the brain

Li-Yueh Hsu, Murray Loew

Proceedings Volume 3905, (2000) https://doi.org/10.1117/12.384878

KEYWORDS: Image registration, Image segmentation, Feature extraction, Magnetic resonance imaging, Brain, Edge detection, Computed tomography, Neuroimaging, 3D image processing, Positron emission tomography

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Proceedings Article | 21 May 1999 Paper

Hierarchical approach for automated segmentation of the brain volume from MR images

Li-Yueh Hsu, Murray Loew, Reza Momenan

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348492

KEYWORDS: Brain, Image segmentation, Magnetic resonance imaging, Neuroimaging, Tissues, Binary data, Head, Quantitative analysis, Image registration, Image processing algorithms and systems

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Image segmentation is considered one of the essential steps in medical image analysis. Cases such as classification of tissue structures for quantitative analysis, reconstruction of anatomical volumes for visualization, and registration of multi-modality images for complementary study often require the segmentation of the brain to accomplish the task. In many clinical applications, parts of this task are performed either manually or interactively. Not only is this proces often tedious and time-consuming, it introduces additional external factors of inter- and intra-rater variability. In this paper, we present a 3D automated algorithm for segmenting the brain from various MR images. This algorithm consists of a sequence of pre-determined steps: First, an intensity window for initial separation of the brain volume from the background and non-brain structures is selected by using probability curves fitting on the intensity histogram. Next, a 3D isotropic volume is interpolated and an optimal threshold value is determined to construct a binary brain mask. The morphological and connectivity processes are then applied on this 3D mask for eliminating the non-brain structures. Finally, a surface extraction kernel is applied to extract the 3D brain surface. Preliminary results from the same subjects with different pulse sequences are compared with the manual segmentation. The automatically segmented brain volumes are compared with the manual results using the correlation coefficient and percentage overlay. Then the automatically detected surfaces are measured with the manual contouring in terms of RMS distance. The introduced automatic segmentation algorithm is effective on different sequences of MR data sets without any parameter tuning. It requires no user interaction so variability introduced by manual tracing or interactive thresholding can be eliminated. Currently, the introduced segmentation algorithm is applied in the automated inter- and intra-modality image registration. It will furthermore be used in different applications such as quantitative analysis of normal and abnormal brain tissues.

Proceedings Article | 21 May 1999 Paper

Multimodality image registration based on hierarchical shape representation

Li-Yueh Hsu, Murray Loew, John Ostuni

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348639

KEYWORDS: Image registration, Computed tomography, Magnetic resonance imaging, 3D image processing, Feature extraction, Edge detection, Brain, Natural surfaces, Skull, Neuroimaging

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Showing 5 of 9 publications

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