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105 条记录 (耗时 0.036 秒)

    System and method for quasi-real-time ventricular measurements from M-mode echocardiogram
    31.
    发明授权
    System and method for quasi-real-time ventricular measurements from M-mode echocardiogram 有权
    从M型超声心动图准准实时心室测量的系统和方法

    公开(公告)号：US08396531B2

    公开(公告)日：2013-03-12

    申请号：US12054094

    申请日：2008-03-24

    申请人： Shaohua Kevin Zhou ,  Feng Guo ,  John Jackson ,  Michael Brendel ,  Dorin Comaniciu

    发明人： Shaohua Kevin Zhou ,  Feng Guo ,  John Jackson ,  Michael Brendel ,  Dorin Comaniciu

    IPC分类号： A61B5/00

    CPC分类号： A61B8/08 ,  A61B5/1075 ,  A61B5/7267 ,  A61B6/503 ,  A61B8/0883 ,  A61B8/463 ,  A61B8/486 ,  G06K9/00523 ,  G06K9/00536 ,  G06K9/6257 ,  G06T7/60

    摘要： A method for measuring ventricular dimensions from M-mode echocardiograms, includes providing a digitized M-mode echocardiogram image, running a plurality of local classifiers, where each local classifier trained to detect a landmark on either an end-diastole (ED) line or an end-systole (ES) line in the image, recording all possible landmarks detected by the classifiers, where a search range in an N-dimensional parameter space defined by the landmarks for each dimension is reduced to a union of subsets, where each dimension of the parameter space corresponds a landmark, for each combination of possible landmarks, checking if an order of the landmarks is consistent with a known ordering of the landmarks, and if the order is consistent, running a global detector on each consistent combination of landmarks to find a landmark combination with a highest detection probability as a confirmed landmark detection, where the landmarks are used for measuring ventricular dimensions.

    摘要翻译： 一种用于从M型超声心动图测量心室尺寸的方法，包括提供数字化的M模式超声心动图图像，运行多个局部分类器，其中训练每个局部分类器以检测舒张末期（ED）线或 记录图像中的终点收缩（ES）线，记录由分类器检测到的所有可能的地标，其中由每个维度的地标定义的N维参数空间中的搜索范围被减少到子集的并集，其中每个维度 参数空间对应于地标，对于可能的地标的每个组合，检查地标的顺序是否与已知的地标的顺序一致，并且如果顺序一致，则在每个一致的地标组合上运行全局检测器以找到 具有最高检测概率的地标组合，作为确认的地标检测，其中地标被用于测量心室维度。

    System and method for learning relative distance in a shape space using image based features
    35.
    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。

    Scalable Semantic Image Search
    36.
    发明申请
    Scalable Semantic Image Search 审中-公开
    可扩展语义图像搜索

    公开(公告)号：US20080027917A1

    公开(公告)日：2008-01-31

    申请号：US11767920

    申请日：2007-06-25

    申请人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

    发明人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

    IPC分类号： G06F17/30

    CPC分类号： G06F16/583 ,  G06N5/022

    摘要： A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

    摘要翻译： 一种用于搜索感兴趣的图像的多个图像的计算机实现的系统，包括与多个图像相对应的语义图像表示的数据库，其中所述语义图像表示链接临床属性的语义模型，高级图像的句法模型 低级图像属性的属性和图像词汇表，与语义图像表示相关联的一组查询，以及体现为由处理器执行的计算机可读代码的语义搜索引擎，用于接收搜索查询，选择至少一个 基于搜索查询的查询集合，以及通过将多个图像与与所选择的查询相关联的语义图像表示进行比较来搜索多个图像中的感兴趣图像。

    Method and system for automatic native and bypass coronary ostia detection in cardiac computed tomography volumes
    38.
    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。

    Data transmission in remote computer assisted detection
    39.
    发明授权
    Data transmission in remote computer assisted detection 有权
    远程计算机辅助检测中的数据传输

    公开(公告)号：US08811697B2

    公开(公告)日：2014-08-19

    申请号：US13080891

    申请日：2011-04-06

    申请人： Michal Sofka ,  Kristof Ralovich ,  Jingdan Zhang ,  Shaohua Kevin Zhou ,  Gianluca Paladini ,  Dorin Comaniciu

    发明人： Michal Sofka ,  Kristof Ralovich ,  Jingdan Zhang ,  Shaohua Kevin Zhou ,  Gianluca Paladini ,  Dorin Comaniciu

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

    CPC分类号： G06K9/6256 ,  G06F19/00 ,  G06F19/321 ,  G06T7/0012 ,  G06T2200/16 ,  G06T2207/20016

    摘要： For cloud-based computer assisted detection, hierarchal detection is used, allowing detection on data at progressively greater resolutions. Detected locations at coarser resolutions are used to limit the data transmitted at greater resolutions. Data is only transmitted for neighborhoods around the previously detected locations. Subsequent detection using higher resolution data refines the locations, but only for regions associated with previous detection. By limiting the number and/or size of regions provided at greater resolutions based on the previous detection, the progressive transmission avoids transmission of some data. Additionally, or alternatively, lossy compression may be used without or with minimal reduction in detection sensitivity.

    摘要翻译： 对于基于云的计算机辅助检测，使用层次检测，允许以逐渐更大的分辨率检测数据。 较粗分辨率下的检测位置用于限制以更高分辨率传输的数据。 仅针对先前检测到的位置周围的邻域发送数据。 使用较高分辨率数据的后续检测会优化位置，但仅适用于与先前检测相关的区域。 通过基于先前的检测限制以更大分辨率提供的区域的数量和/或尺寸，逐行传输避免了一些数据的传输。 另外或者可选地，可以使用有损压缩，而不用或以最小的检测灵敏度降低。