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    Method and system for detection of deformable structures in medical images
    6.
    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。

    Method and system for detection of deformable structures in medical images
    8.
    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。

    System and method for detection of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree
    9.
    发明授权
    System and method for detection of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree 有权
    使用约束概率增强树从超声图像中检测胎儿解剖结构的系统和方法

    公开(公告)号：US07995820B2

    公开(公告)日：2011-08-09

    申请号：US12056107

    申请日：2008-03-26

    申请人： Gustavo Henrique Monteiro de Barros Carneiro ,  Bogdan Georgescu ,  Sara Good ,  Dorin Comaniciu

    发明人： Gustavo Henrique Monteiro de Barros Carneiro ,  Bogdan Georgescu ,  Sara Good ,  Dorin Comaniciu

    IPC分类号： G06K9/00

    CPC分类号： G06K9/46 ,  A61B8/0866 ,  G06K9/3233 ,  G06K9/38 ,  G06K9/4614 ,  G06K9/6282 ,  G06K2009/366 ,  G06K2209/051

    摘要： A method for detecting fetal anatomic features in ultrasound images includes providing an ultrasound image of a fetus, specifying an anatomic feature to be detected in a region S determined by parameter vector θ, providing a sequence of probabilistic boosting tree classifiers, each with a pre-specified height and number of nodes. Each classifier computes a posterior probability P(y|S) where yε{−1,+1}, with P(y=+1|S) representing a probability that region S contains the feature, and P(y=−1|S) representing a probability that region S contains background information. The feature is detected by uniformly sampling a parameter space of parameter vector θ using a first classifier with a sampling interval vector used for training said first classifier, and having each subsequent classifier classify positive samples identified by a preceding classifier using a smaller sampling interval vector used for training said preceding classifier. Each classifier forms a union of its positive samples with those of the preceding classifier.

    摘要翻译： 一种用于检测超声图像中的胎儿解剖特征的方法，包括提供胎儿的超声图像，指定在由参数矢量和姿势确定的区域S中要检测的解剖特征;提供一系列概率增强树分类器，每个具有预先 指定的高度和节点数。 每个分类器计算出一个后验概率P（y | S），其中y（e）= { - 1，+ 1}，其中P（y = + 1 | S）表示区域S包含特征的概率，P（y = -1 | S），表示区域S包含背景信息的概率。 通过对参数矢量和参数的参数空间进行均匀采样来检测该特征; 使用具有用于训练所述第一分类器的采样间隔向量的第一分类器，并且每个后续分类器使用用于训练所述先前分类器的较小采样间隔向量来对由先前分类器标识的正样本进行分类。 每个分类器形成其正样本与上一分类器的并集。