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    System and method for learning relative distance in a shape space using image based features
    1.
    发明授权
    System and method for learning relative distance in a shape space using image based features 有权
    使用基于图像的特征来学习形状空间中的相对距离的系统和方法

    公开(公告)号：US07603000B2

    公开(公告)日：2009-10-13

    申请号：US11464851

    申请日：2006-08-16

    申请人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Kevin Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Kevin Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC分类号： G06K9/60

    CPC分类号： G06K9/6255 ,  G06K9/6206 ,  G06K9/6256

    摘要： A system and method for identifying a shape of an anatomical structure in an input image is disclosed. An input image is received and warped using a set of warping templates resulting in a set of warped images. An integral image is calculated for each warped image. Selected features are extracted based on the integral image. A boosted feature score is calculated for the combined selected features for each warped image. The warped images are ranked based on the boosted feature scores. A predetermined number of warped images are selected that have the largest feature scores. Each selected warped image is associated with its corresponding warping template. The corresponding warping templates are associated with stored shape models. The shape of the input image is identified based on the weighted average of the shapes models.

    摘要翻译： 公开了一种用于识别输入图像中的解剖结构的形状的系统和方法。 使用一组翘曲模板接收和扭曲输入图像，产生一组翘曲图像。 为每个弯曲图像计算整体图像。 基于积分图像提取所选特征。 对于每个弯曲图像的组合选定特征，计算提升的特征分数。 翘曲的图像根据提升的特征得分进行排名。 选择具有最大特征分数的预定数量的翘曲图像。 每个选择的变形图像与其相应的翘曲模板相关联。 相应的变形模板与存储的形状模型相关联。 基于形状模型的加权平均值来识别输入图像的形状。

    SYSTEM AND METHOD FOR LEARNING RELATIVE DISTANCE IN A SHAPE SPACE USING IMAGE BASED FEATURES
    6.
    发明申请
    SYSTEM AND METHOD FOR LEARNING RELATIVE DISTANCE IN A SHAPE SPACE USING IMAGE BASED FEATURES 有权
    使用基于图像的特征在形状空间中学习相对距离的系统和方法

    公开(公告)号：US20070046696A1

    公开(公告)日：2007-03-01

    申请号：US11464851

    申请日：2006-08-16

    申请人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC分类号： G09G5/00

    CPC分类号： G06K9/6255 ,  G06K9/6206 ,  G06K9/6256

    摘要： A system and method for identifying a shape of an anatomical structure in an input image is disclosed. An input image is received and warped using a set of warping templates resulting in a set of warped images. An integral image is calculated for each warped image. Selected features are extracted based on the integral image. A boosted feature score is calculated for the combined selected features for each warped image. The warped images are ranked based on the boosted feature scores. A predetermined number of warped images are selected that have the largest feature scores. Each selected warped image is associated with its corresponding warping template. The corresponding warping templates are associated with stored shape models. The shape of the input image is identified based on the weighted average of the shapes models.

    摘要翻译： 公开了一种用于识别输入图像中的解剖结构的形状的系统和方法。 使用一组翘曲模板接收和扭曲输入图像，产生一组翘曲图像。 为每个弯曲图像计算整体图像。 基于积分图像提取所选特征。 对于每个弯曲图像的组合选定特征，计算提升的特征分数。 翘曲的图像根据提升的特征得分进行排名。 选择具有最大特征分数的预定数量的翘曲图像。 每个选择的变形图像与其相应的翘曲模板相关联。 相应的变形模板与存储的形状模型相关联。 基于形状模型的加权平均值来识别输入图像的形状。

    Method and system for automatic native and bypass coronary ostia detection in cardiac computed tomography volumes
    7.
    发明授权
    Method and system for automatic native and bypass coronary ostia detection in cardiac computed tomography volumes 有权
    心脏计算机断层扫描体积中自动原位和旁路冠状动脉口腔检测的方法和系统

    公开(公告)号：US09042619B2

    公开(公告)日：2015-05-26

    申请号：US13233220

    申请日：2011-09-15

    申请人： Yefeng Zheng ,  Fernando Vega-Higuera ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Fernando Vega-Higuera ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    IPC分类号： G06K9/00 ,  G06K9/62 ,  G06K9/34 ,  G06T7/00

    CPC分类号： G06T7/0081 ,  G06T7/11 ,  G06T7/12 ,  G06T2207/10072 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06T2207/30172

    摘要： A method and system for detection of native and bypass coronary ostia in a 3D volume, such as a CT volume, is disclosed. Native coronary ostia are detected by detecting a bounding box defining locations of a left native coronary ostium and a right native coronary ostium in the 3D volume using marginal space learning (MSL), and locally refining the locations of the left native coronary ostium and the right native coronary ostium using a trained native coronary ostium detector. Bypass coronary ostia are detected by segmenting an ascending aorta surface mesh in the 3D volume, generating a search region of a plurality of mesh points on the ascending aorta surface mesh based on a distribution of annotated bypass coronary ostia in a plurality of training volumes, and detecting the bypass coronary ostia by searching the plurality of mesh points in the search region.

    摘要翻译： 公开了用于检测3D体积中的天然和旁路冠状动脉口腔的方法和系统，例如CT体积。 通过使用边缘空间学习（MSL）检测在3D体积中定义左天然冠状动脉口和右天然冠状动脉口的位置的边界框来检测本地冠状动脉，并且局部改善左天然冠状动脉口和右侧的位置 使用训练有素的本地冠状动脉口腔检测器进行本地冠状动脉口。 通过分割3D体积中的上升主动脉表面网格来检测旁路冠状动脉口腔，基于多个训练体积中注释旁路冠状动脉口腔的分布，生成升主动脉表面网格上的多个网格点的搜索区域，以及 通过搜索搜索区域中的多个网格点来检测旁路冠状动脉口。

    Method and System for Automatic Native and Bypass Coronary Ostia Detection in Cardiac Computed Tomography Volumes
    9.
    发明申请
    Method and System for Automatic Native and Bypass Coronary Ostia Detection in Cardiac Computed Tomography Volumes 有权
    心脏计算机断层扫描中自动本机和旁路冠状动脉检测的方法和系统

    公开(公告)号：US20120071755A1

    公开(公告)日：2012-03-22

    申请号：US13233220

    申请日：2011-09-15

    申请人： Yefeng Zheng ,  Fernando Vega-Higuera ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Fernando Vega-Higuera ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    IPC分类号： A61B6/03

    CPC分类号： G06T7/0081 ,  G06T7/11 ,  G06T7/12 ,  G06T2207/10072 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06T2207/30172

    摘要： A method and system for detection of native and bypass coronary ostia in a 3D volume, such as a CT volume, is disclosed. Native coronary ostia are detected by detecting a bounding box defining locations of a left native coronary ostium and a right native coronary ostium in the 3D volume using marginal space learning (MSL), and locally refining the locations of the left native coronary ostium and the right native coronary ostium using a trained native coronary ostium detector. Bypass coronary ostia are detected by segmenting an ascending aorta surface mesh in the 3D volume, generating a search region of a plurality of mesh points on the ascending aorta surface mesh based on a distribution of annotated bypass coronary ostia in a plurality of training volumes, and detecting the bypass coronary ostia by searching the plurality of mesh points in the search region.

    摘要翻译： 公开了用于检测3D体积中的天然和旁路冠状动脉口腔的方法和系统，例如CT体积。 通过使用边缘空间学习（MSL）检测在3D体积中定义左天然冠状动脉口和右天然冠状动脉口的位置的边界框来检测本地冠状动脉，并且局部改善左天然冠状动脉口和右侧的位置 使用训练有素的本地冠状动脉口腔检测器进行本地冠状动脉口。 通过分割3D体积中的上升主动脉表面网格来检测旁路冠状动脉口腔，基于多个训练体积中注释旁路冠状动脉口腔的分布，生成升主动脉表面网格上的多个网格点的搜索区域，以及 通过搜索搜索区域中的多个网格点来检测旁路冠状动脉口。