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41.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
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。
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42.发明申请Method and System for Multi-Component Heart and Aorta Modeling for Decision Support in Cardiac Disease 失效多组分心脏和主动脉建模心脏疾病决策支持的方法与系统公开(公告)号：US20100280352A1
公开(公告)日：2010-11-04
申请号：US12770850
申请日：2010-04-30
申请人： Razvan Ioan Ionasec , Puneet Sharma , Bogdan Georgescu , Andrey Torzhkov , Fabian Moerchen , Gayle M. Wittenberg , Dmitriy Fradkin , Dorin Comaniciu
发明人： Razvan Ioan Ionasec , Puneet Sharma , Bogdan Georgescu , Andrey Torzhkov , Fabian Moerchen , Gayle M. Wittenberg , Dmitriy Fradkin , Dorin Comaniciu
CPC分类号： A61B5/0263 , G01R33/56 , G01R33/5608 , G06F19/00 , G06F19/321 , G06T17/00 , G16H50/20 , G16H50/50
摘要： A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.
摘要翻译： 公开了一种用于产生患者特异性解剖心脏模型的方法和系统。 接收患者心脏区域的体积图像数据，例如计算机断层摄影（CT），超声心动图或磁共振（MR）图像数据。 用于多个心脏组件的单独模型，例如左心室（LV）心内膜，LV心外膜，右心室（RV），左心房（LA），右心房（RA），二尖瓣，主动脉瓣，主动脉和肺动脉干， 在所述体积心脏图像数据中估计。 通过对每个心脏组件的各个模型进行整合，产生多组分患者特异性解剖心脏模型。 对患者特异性解剖模型进行流体结构相互作用（FSI）模拟，并根据患者特异性心脏模型和FSI模拟提取患者特异性临床参数。 疾病进展模型和风险分层是根据患者的具体临床参数进行的。
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公开(公告)号：US07804999B2
公开(公告)日：2010-09-28
申请号：US11372782
申请日：2006-03-10
CPC分类号： G06K9/6256 , G06K2009/00322 , G06K2209/05
摘要： A method for performing image based regression using boosting to infer an entity that is associated with an image of an object is disclosed. A regression function for a plurality of images is learned in which for each image the associated entity is known. The learned regression function is used to predict an entity associated with an image in which the entity is not known.
摘要翻译： 公开了一种使用增强来执行基于图像的回归的方法来推断与对象的图像相关联的实体。 学习用于多个图像的回归函数，其中对于每个图像，相关联的实体是已知的。 学习的回归函数用于预测与实体不知道的图像相关联的实体。
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公开(公告)号：US20100240996A1
公开(公告)日：2010-09-23
申请号：US12702036
申请日：2010-02-08
申请人： Razvan Ioan Ionasec , Ingmar Voigt , Yang Wang , Bogdan Georgescu , Helene C. Houle , Dorin Comaniciu , Fernando Vega-Higuera
发明人： Razvan Ioan Ionasec , Ingmar Voigt , Yang Wang , Bogdan Georgescu , Helene C. Houle , Dorin Comaniciu , Fernando Vega-Higuera
CPC分类号： G06T7/0016 , G06T7/251 , G06T7/262 , G06T7/277 , G06T2207/10016 , G06T2207/10076 , G06T2207/10136 , G06T2207/20016 , G06T2207/30048 , G06T2207/30241
摘要： Heart valve operation is assessed with patient-specific medical diagnostic imaging data. To deal with the complex motion of the passive valve tissue, a hierarchal model is used. Rigid global motion of the overall valve, non-rigid local motion of landmarks of the valve, and surface motion of the valve are modeled sequentially. For the non-rigid local motion, a spectral trajectory approach is used in the model to determine location and motion of the landmarks more efficiently than detection and tracking. Given efficiencies in processing, more than one valve may be modeled at a same time. A graphic overlay representing the valve in four dimensions and/or quantities may be provided during an imaging session. One or more of these features may be used in combination or independently.
摘要翻译： 心脏瓣膜手术用患者特定的医学诊断成像数据进行评估。 为了处理被动瓣膜组织的复杂运动，使用了层次模型。 总体阀门的刚性全局运动，阀门的标志的非刚性局部运动以及阀门的表面运动被依次建模。 对于非刚性局部运动，在模型中使用频谱轨迹方法，以比检测和跟踪更有效地确定地标的位置和运动。 在处理效率方面，可以同时对多个阀进行建模。 可以在成像会话期间提供表示四维和/或数量的阀的图形覆盖。 这些特征中的一个或多个可以组合使用或独立使用。
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45.发明申请Method and System for Automatic Landmark Detection Using Discriminative Joint Context 有权使用歧义联合上下文的自动地标检测方法与系统公开(公告)号：US20100067764A1
公开(公告)日：2010-03-18
申请号：US12549461
申请日：2009-08-28
IPC分类号： G06K9/00
CPC分类号： G06K9/72 , G06T7/13 , G06T7/73 , G06T2207/10088 , G06T2207/20164 , G06T2207/30048
摘要： A method and system for detecting anatomic landmarks in medical images is disclosed. In order to detect multiple related anatomic landmarks, a plurality of landmark candidates are first detected individually using trained landmark detectors. A joint context is then generated for each combination of the landmark candidates. The best combination of landmarks in then determined based on the joint context using a trained joint context detector.
摘要翻译： 公开了用于检测医学图像中的解剖学标记的方法和系统。 为了检测多个相关的解剖标志，首先使用训练有素的地标检测器单独检测多个地标候选物。 然后为地标候选人的每个组合生成联合上下文。 使用训练有素的联合上下文检测器，基于联合上下文确定的地标的最佳组合。
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公开(公告)号：US07558402B2
公开(公告)日：2009-07-07
申请号：US10794476
申请日：2004-03-05
CPC分类号： G06K9/6247 , G06K9/3216 , G06K9/629 , G06K2209/051 , G06T7/215 , G06T7/251 , G06T2207/10132 , G06T2207/30048
摘要： A system and method for tracking a global shape of an object in motion is disclosed. One or more control points along an initial contour of the global shape are defined. Each of the one or more control points is tracked as the object is in motion. Uncertainty of a location of a control point in motion is represented using a number of techniques. The uncertainty to constrain the global shape is exploited using a prior shape model. In an alternative embodiment, multiple appearance models are built for each control point and the motion vectors produced by each model are combined in order to track the shape of the object. The movement of the shape of the object can be visually tracked using a display and color vectors.
摘要翻译： 公开了一种跟踪运动对象的全局形状的系统和方法。 定义沿着全局形状初始轮廓的一个或多个控制点。 一个或多个控制点中的每一个在物体运动时被跟踪。 使用许多技术来表示控制点在运动中的位置的不确定性。 使用先前的形状模型来利用限制全局形状的不确定性。 在替代实施例中，为每个控制点构建多个外观模型，并且组合由每个模型产生的运动矢量以跟踪对象的形状。 可以使用显示和颜色向量来可视地跟踪对象的形状的移动。
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47.发明授权System and method for detecting and matching anatomical structures using appearance and shape 有权使用外观和形状检测和匹配解剖结构的系统和方法公开(公告)号：US07536044B2
公开(公告)日：2009-05-19
申请号：US10991933
申请日：2004-11-18
CPC分类号： G06K9/6206 , G06K9/6256 , G06T7/12 , G06T7/149 , G06T7/251 , G06T2207/10132 , G06T2207/30048
摘要： A detection framework that matches anatomical structures using appearance and shape is disclosed. A training set of images are used in which object shapes or structures are annotated in the images. A second training set of images represents negative examples for such shapes and structures, i.e., images containing no such objects or structures. A classification algorithm trained on the training sets is used to detect a structure at its location. The structure is matched to a counterpart in the training set that can provide details about the structure's shape and appearance.
摘要翻译： 公开了一种使用外观和形状匹配解剖结构的检测框架。 使用图像的训练集，其中在图像中注释对象形状或结构。 图像的第二训练集表示这种形状和结构的负面例子，即不包含这样的对象或结构的图像。 训练集训练的分类算法用于检测其位置的结构。 该结构与训练集中的对应物匹配，可以提供关于结构形状和外观的细节。
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48.发明申请System and Method For Using A Similarity Function To Perform Appearance Matching In Image Pairs 有权在图像对中使用相似性函数进行外观匹配的系统和方法公开(公告)号：US20070237370A1
公开(公告)日：2007-10-11
申请号：US11539989
申请日：2006-10-10
申请人： Shaohua Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： G06K9/00
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。
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公开(公告)号：US20070071313A1
公开(公告)日：2007-03-29
申请号：US11372782
申请日：2006-03-10
申请人： Shaohua Zhou , Bogdan Georgescu , Xiang Zhou , Dorin Comaniciu
发明人： Shaohua Zhou , Bogdan Georgescu , Xiang Zhou , Dorin Comaniciu
IPC分类号： G06K9/62
CPC分类号： G06K9/6256 , G06K2009/00322 , G06K2209/05
摘要： A method for performing image based regression using boosting to infer an entity that is associated with an image of an object is disclosed. A regression function for a plurality of images is learned in which for each image the associated entity is known. The learned regression function is used to predict an entity associated with an image in which the entity is not known.
摘要翻译： 公开了一种使用增强来执行基于图像的回归的方法来推断与对象的图像相关联的实体。 学习用于多个图像的回归函数，其中对于每个图像，相关联的实体是已知的。 学习的回归函数用于预测与实体不知道的图像相关联的实体。
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50.发明申请Method and system for multi-modal component-based tracking of an object using robust information fusion 失效使用鲁棒信息融合的多模态组件跟踪对象的方法和系统公开(公告)号：US20050185826A1
公开(公告)日：2005-08-25
申请号：US11058784
申请日：2005-02-16
申请人： Bogdan Georgescu , Xiang Zhou , Dorin Comaniciu , R. Rao
发明人： Bogdan Georgescu , Xiang Zhou , Dorin Comaniciu , R. Rao
CPC分类号： G06K9/3216 , G06K9/6206 , G06T7/215 , G06T7/269
摘要： A system and method for tracking an object is disclosed. A video sequence including a plurality of image frames are received. A sample based representation of object appearance distribution is maintained. An object is divided into one or more components. For each component, its location and uncertainty with respect to the sample based representation are estimated. Variable-Bandwidth Density Based Fusion (VBDF) is applied to each component to determine a most dominant motion. The motion estimate is used to determine the track of the object.
摘要翻译： 公开了一种跟踪对象的系统和方法。 接收包括多个图像帧的视频序列。 维护对象外观分布的基于样本的表示。 一个对象被分成一个或多个组件。 对于每个组件，估计其相对于基于样本的表示的位置和不确定性。 基于可变带宽密度的融合（VBDF）应用于每个组件以确定最主要的运动。 运动估计用于确定对象的轨迹。
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