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    Automatic cardiac view classification of echocardiography
    1.
    发明授权
    Automatic cardiac view classification of echocardiography 有权
    自动心脏超声心动图分类

    公开(公告)号：US08170303B2

    公开(公告)日：2012-05-01

    申请号：US11775538

    申请日：2007-07-10

    申请人： Shaohua Kevin Zhou ,  Jin-hyeong Park ,  Joanne Otsuki ,  Dorin Comaniciu ,  Bogdan Georgescu ,  Constantine Simopoulos

    发明人： Shaohua Kevin Zhou ,  Jin-hyeong Park ,  Joanne Otsuki ,  Dorin Comaniciu ,  Bogdan Georgescu ,  Constantine Simopoulos

    IPC分类号： G06K9/00

    CPC分类号： G06K9/6256 ,  G06K9/6292 ,  G06K2209/051

    摘要： A method for view classification includes providing a frame of an object of interest, detecting a region of interest within the object of interest for each of a plurality of detectors (e.g., binary classifiers), wherein each binary classifier corresponds to a different view, performing a global view classification using a multiview classifier for each view, outputting a classification for each view, fusing outputs of the multiview classifiers, and determining and outputting a classification of the frame based on a fused output of the multiview classifiers.

    摘要翻译： 一种用于视图分类的方法包括提供感兴趣对象的帧，检测多个检测器（例如，二进制分类器）中的每一个的感兴趣对象内的感兴趣区域，其中每个二进制分类器对应于不同视图，执行 对于每个视图使用多视角分类器的全局视图分类，输出每个视图的分类，融合多视角分类器的输出，以及基于多视角分类器的融合输出来确定和输出该帧的分类。

    Automatic Cardiac View Classification of Echocardiography
    2.
    发明申请
    Automatic Cardiac View Classification of Echocardiography 有权
    超声心动图自动心脏视图分类

    公开(公告)号：US20090034808A1

    公开(公告)日：2009-02-05

    申请号：US11775538

    申请日：2007-07-10

    申请人： Shaohua Kevin Zhou ,  Jin-Hyeong Park ,  Joanne Otsuki ,  Dorin Comaniciu ,  Bogdan Georgescu ,  Constantine Simopoulos

    发明人： Shaohua Kevin Zhou ,  Jin-Hyeong Park ,  Joanne Otsuki ,  Dorin Comaniciu ,  Bogdan Georgescu ,  Constantine Simopoulos

    IPC分类号： G06K9/00

    CPC分类号： G06K9/6256 ,  G06K9/6292 ,  G06K2209/051

    摘要： A method for view classification includes providing a frame of an object of interest, detecting a region of interest within the object of interest for each of a plurality of detectors (e.g., binary classifiers), wherein each binary classifier corresponds to a different view, performing a global view classification using a multiview classifier for each view, outputting a classification for each view, fusing outputs of the multiview classifiers, and determining and outputting a classification of the frame based on a fused output of the multiview classifiers.

    摘要翻译： 一种用于视图分类的方法包括提供感兴趣对象的帧，检测多个检测器（例如，二进制分类器）中的每一个的感兴趣对象内的感兴趣区域，其中每个二进制分类器对应于不同视图，执行 对于每个视图使用多视角分类器的全局视图分类，输出每个视图的分类，融合多视角分类器的输出，以及基于多视角分类器的融合输出来确定和输出该帧的分类。

    Method and system for detection of deformable structures in medical images
    3.
    发明申请
    Method and system for detection of deformable structures in medical images 有权
    用于检测医学图像中可变形结构的方法和系统

    公开(公告)号：US20090010509A1

    公开(公告)日：2009-01-08

    申请号：US12214339

    申请日：2008-06-18

    申请人： Shaohua Kevin Zhou ,  Feng Guo ,  Jin-Hyeong Park ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu ,  John I. Jackson

    发明人： Shaohua Kevin Zhou ,  Feng Guo ,  Jin-Hyeong Park ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu ,  John I. Jackson

    IPC分类号： G06K9/62

    CPC分类号： G06K9/0055 ,  G06K9/6206 ,  G06K9/6256

    摘要： A method and system for detection of deformable structures in medical images is disclosed. Deformable structures can represent blood flow patterns in images such as Doppler echocardiograms. A probabilistic, hierarchical, and discriminant framework is used to detect such deformable structures. This framework integrates evidence from different primitive levels via a progressive detector hierarchy, including a series of discriminant classifiers. A target deformable structure is parameterized by a multi-dimensional parameter, and primitives or partial parameterizations of the parameter are determined. An input image is received, and a series of primitives are sequentially detected using the progressive detector hierarchy, in which each detector or classifier detects a corresponding primitive. The final detector detects configuration candidates for the deformable structure.

    摘要翻译： 公开了用于检测医学图像中的可变形结构的方法和系统。 可变形结构可以表示图像中的血流模式，例如多普勒超声心动图。 概率，分层和判别框架用于检测这种可变形结构。 该框架通过渐进式检测器层次结合不同原始级别的证据，包括一系列判别分类器。 目标可变形结构由多维参数进行参数化，并确定参数的基元或部分参数化。 接收输入图像，并且使用逐行检测器层级顺序地检测一系列图元，其中每个检测器或分类器检测相应的图元。 最终检测器检测可变形结构的配置候选。

    Automated view classification with echocardiographic data for gate localization or other purposes
    4.
    发明授权
    Automated view classification with echocardiographic data for gate localization or other purposes 有权
    用于门定位或其他目的的超声心动图数据的自动视图分类

    公开(公告)号：US08092388B2

    公开(公告)日：2012-01-10

    申请号：US12210419

    申请日：2008-09-15

    申请人： Jin Hyeong Park ,  Shaohua Kevin Zhou ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu

    发明人： Jin Hyeong Park ,  Shaohua Kevin Zhou ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu

    IPC分类号： A61B8/00 ,  A61B8/08

    CPC分类号： G06K9/6257 ,  G06K9/6292 ,  G06K2209/05

    摘要： A view represented by echocardiographic data is classified. A probabilistic boosting network is used to classify the view. The probabilistic boosting network may include multiple levels where each level has a multi-class local structure classifier and a plurality of local-structure detectors corresponding to the respective multiple classes. In each level, the local structure is classified as a particular view and then the local structure is detected to determine whether the currently selected local structure corresponds to the class. The view classification may be used to determine gate locations, such as a gate for spectral Doppler analysis.

    摘要翻译： 以超声心动图数据为代表的视图进行分类。 概率增强网络用于对视图进行分类。 概率增强网络可以包括多个级别，其中每个级别具有多类本地结构分类器和对应于相应的多个类别的多个局部结构检测器。 在每个级别中，将本地结构分类为特定视图，然后检测本地结构以确定当前选择的本地结构是否对应于该类。 视图分类可用于确定门位置，例如用于频谱多普勒分析的门。

    Automated View Classification With Echocardiographic Data For Gate Localization Or Other Purposes
    5.
    发明申请
    Automated View Classification With Echocardiographic Data For Gate Localization Or Other Purposes 有权
    自动视图分类与超声心动图数据门定位或其他目的

    公开(公告)号：US20090088640A1

    公开(公告)日：2009-04-02

    申请号：US12210419

    申请日：2008-09-15

    申请人： Jin Hyeong Park ,  Shaohua Kevin Zhou ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu

    发明人： Jin Hyeong Park ,  Shaohua Kevin Zhou ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu

    IPC分类号： A61B8/00 ,  G06F17/30

    CPC分类号： G06K9/6257 ,  G06K9/6292 ,  G06K2209/05

    摘要： A view represented by echocardiographic data is classified. A probabilistic boosting network is used to classify the view. The probabilistic boosting network may include multiple levels where each level has a multi-class local structure classifier and a plurality of local-structure detectors corresponding to the respective multiple classes. In each level, the local structure is classified as a particular view and then the local structure is detected to determine whether the currently selected local structure corresponds to the class. The view classification may be used to determine gate locations, such as a gate for spectral Doppler analysis.

    摘要翻译： 以超声心动图数据为代表的视图进行分类。 概率增强网络用于对视图进行分类。 概率增强网络可以包括多个级别，其中每个级别具有多类本地结构分类器和对应于相应的多个类别的多个局部结构检测器。 在每个级别中，将本地结构分类为特定视图，然后检测本地结构以确定当前选择的本地结构是否对应于该类。 视图分类可用于确定门位置，例如用于频谱多普勒分析的门。

    Method and system for detection of deformable structures in medical images
    6.
    发明授权
    Method and system for detection of deformable structures in medical images 有权
    用于检测医学图像中可变形结构的方法和系统

    公开(公告)号：US08150116B2

    公开(公告)日：2012-04-03

    申请号：US12214339

    申请日：2008-06-18

    申请人： Shaohua Kevin Zhou ,  Feng Guo ,  Jin-hyeong Park ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu ,  John I. Jackson

    发明人： Shaohua Kevin Zhou ,  Feng Guo ,  Jin-hyeong Park ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu ,  John I. Jackson

    IPC分类号： G06K17/00

    CPC分类号： G06K9/0055 ,  G06K9/6206 ,  G06K9/6256

    摘要： A method and system for detection of deformable structures in medical images is disclosed. Deformable structures can represent blood flow patterns in images such as Doppler echocardiograms. A probabilistic, hierarchical, and discriminant framework is used to detect such deformable structures. This framework integrates evidence from different primitive levels via a progressive detector hierarchy, including a series of discriminant classifiers. A target deformable structure is parameterized by a multi-dimensional parameter, and primitives or partial parameterizations of the parameter are determined. An input image is received, and a series of primitives are sequentially detected using the progressive detector hierarchy, in which each detector or classifier detects a corresponding primitive. The final detector detects configuration candidates for the deformable structure.

    摘要翻译： 公开了用于检测医学图像中的可变形结构的方法和系统。 可变形结构可以表示图像中的血流模式，例如多普勒超声心动图。 概率，分层和判别框架用于检测这种可变形结构。 该框架通过渐进式检测器层次结合不同原始级别的证据，包括一系列判别分类器。 目标可变形结构通过多维参数进行参数化，并确定参数的基元或部分参数化。 接收输入图像，并且使用逐行检测器层级顺序地检测一系列图元，其中每个检测器或分类器检测相应的图元。 最终检测器检测可变形结构的配置候选。

    Phase unwrapped velocity display for ultrasound medical imaging
    10.
    发明授权
    Phase unwrapped velocity display for ultrasound medical imaging 有权
    用于超声医学成像的相位展开速度显示

    公开(公告)号：US07682311B2

    公开(公告)日：2010-03-23

    申请号：US11233550

    申请日：2005-09-22

    申请人： Constantine Simopoulos ,  Kutay F. Ustuner

    发明人： Constantine Simopoulos ,  Kutay F. Ustuner

    IPC分类号： A61B8/00 ,  G01R23/16 ,  A61B8/14

    CPC分类号： G01S15/584 ,  G01S15/8979

    摘要： Phase unwrapping is applied to velocity data to remove aliasing artifacts. Phase unwrapping is applied on multidimensional regions of velocity data. The unwrapped velocity image is displayed. The displayed image may have high sensitivity to slow motion but may also avoid aliasing of fast motion despite being undersampled.

    摘要翻译： 相位展开应用于速度数据以消除混叠伪像。 相位展开应用于速度数据的多维区域。 显示展开的速度图像。 显示的图像可能对慢动作具有高灵敏度，但也可以避免快速运动的混叠，尽管被欠采样。