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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 regression-based object detection in medical images
    7.
    发明授权
    Method and system for regression-based object detection in medical images 有权
    医学图像中基于回归的物体检测方法与系统

    公开(公告)号：US07949173B2

    公开(公告)日：2011-05-24

    申请号：US11866572

    申请日：2007-10-03

    申请人： Shaohua Kevin Zhou ,  Jinghao Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    发明人： Shaohua Kevin Zhou ,  Jinghao Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC分类号： G06K9/00 ,  A61B6/00

    CPC分类号： G06K9/00 ,  G06K9/6256 ,  G06K9/72 ,  G06K2209/05

    摘要： A method and system for regression-based object detection in medical images is disclosed. A regression function for predicting a location of an object in a medical image based on an image patch is trained using image-based boosting ridge regression (IBRR). The trained regression function is used to determine a difference vector based on an image patch of a medical image. The difference vector represents the difference between the location of the image patch and the location of a target object. The location of the target object in the medical image is predicted based on the difference vector determined by the regression function.

    摘要翻译： 公开了一种用于医学图像中基于回归的物体检测的方法和系统。 使用基于图像的增强脊回归（IBRR）训练用于基于图像块预测医学图像中的对象的位置的回归函数。 训练回归函数用于基于医学图像的图像块来确定差分矢量。 差分向量表示图像块的位置与目标对象的位置之间的差异。 基于由回归函数确定的差分向量来预测目标对象在医学图像中的位置。

    System and method for using a similarity function to perform appearance matching in image pairs
    8.
    发明授权
    System and method for using a similarity function to perform appearance matching in image pairs 有权
    使用相似度函数执行图像对中的外观匹配的系统和方法

    公开(公告)号：US07831074B2

    公开(公告)日：2010-11-09

    申请号：US11539989

    申请日：2006-10-10

    申请人： Shaohua Kevin Zhou ,  Jie Shao ,  Jonathan Dowdall ,  Adrian Barbu ,  Bogdan Georgescu ,  Dorin Comaniciu

    发明人： Shaohua Kevin Zhou ,  Jie Shao ,  Jonathan Dowdall ,  Adrian Barbu ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC分类号： G06K9/00 ,  G06K9/62 ,  H04N5/225

    CPC分类号： G06K9/6257 ,  G06K2209/05 ,  G06T7/33 ,  G06T2207/10124 ,  G06T2207/10132 ,  G06T2207/30048

    摘要： The present invention is directed to a method for populating a database with a set of images of an anatomical structure. The database is used to perform appearance matching in image pairs of the anatomical structure. A set of image pairs of anatomical structures is received, where each image pair is annotated with a plurality of location-sensitive regions that identify a particular aspect of the anatomical structure. Weak learners are iteratively selected and an image patch is identified. A boosting process is used to identify a strong classifier based on responses to the weak learners applied to the identified image patch for each image pair. The responses comprise a feature response and a location response associated with the image patch. Positive and negative image pairs are generated. The positive and negative image pairs are used to learn a similarity function. The learned similarity function and iteratively selected weak learners are stored in the database.

    摘要翻译： 本发明涉及一种用解剖结构的一组图像填充数据库的方法。 该数据库用于在解剖结构的图像对中执行外观匹配。 接收一组解剖结构的图像对，其中每个图像对用多个识别解剖结构的特定方面的位置敏感区域注释。 迭代选择弱学习者，并识别图像补丁。 基于对应用于每个图像对的所识别的图像补丁的弱学习者的响应，使用增强过程来识别强分类器。 响应包括与图像块相关联的特征响应和位置响应。 产生正负图像对。 正负图像对用于学习相似度函数。 学习的相似度函数和迭代选择的弱学习者存储在数据库中。