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11. 发明授权

US08068654B2 Method and system for detection and registration of 3D objects using incremental parameter learning 有权
标题翻译：使用增量参数学习检测和注册3D对象的方法和系统
公开(公告)号：US08068654B2
公开(公告)日：2011-11-29
申请号：US12012386
申请日：2008-02-01
申请人： Adrian Barbu , Le Lu , Luca Bogoni , Marcos Salganicoff , Dorin Comaniciu
发明人： Adrian Barbu , Le Lu , Luca Bogoni , Marcos Salganicoff , Dorin Comaniciu
IPC分类号： G06K9/00 , G06T15/00
CPC分类号： G06K9/32 , G06K9/00201 , G06K9/6256 , G06K2209/051
摘要： A method and system for detecting 3D objects in images is disclosed. In particular, a method and system for Ileo-Cecal Valve detection in 3D computed tomography (CT) images using incremental parameter learning and ICV specific prior learning is disclosed. First, second, and third classifiers are sequentially trained to detect candidates for position, scale, and orientation parameters of a box that bounds an object in 3D image. In the training of each sequential classifier, new training samples are generated by scanning the object's configuration parameters in the current learning projected subspace (position, scale, orientation), based on detected candidates resulting from the previous training step. This allows simultaneous detection and registration of a 3D object with full 9 degrees of freedom. ICV specific prior learning can be used to detect candidate voxels for an orifice of the ICV and to detect initial ICV box candidates using a constrained orientation alignment at each candidate voxel.
摘要翻译：公开了一种用于检测图像中的3D物体的方法和系统。特别地，公开了使用增量参数学习和ICV特有的先前学习的3D计算机断层摄影（CT）图像中Ileo-Cecal Valve检测的方法和系统。顺序训练第一，第二和第三分类器以检测在3D图像中界定对象的框的位置，缩放和取向参数的候选。在每个顺序分类器的训练中，基于从先前的训练步骤得到的检测到的候选，通过在当前学习投影子空间（位置，比例，方向）中扫描对象的配置参数来生成新的训练样本。这允许同时检测和注册具有全9自由度的3D对象。 ICV具体的先验学习可用于检测ICV孔口的候选体素，并使用每个候选体素上的约束取向对齐来检测初始ICV盒候选。

12. 发明申请

US20080211812A1 Method and system for detection and registration of 3D objects using incremental parameter learning 有权
标题翻译：使用增量参数学习检测和注册3D对象的方法和系统
公开(公告)号：US20080211812A1
公开(公告)日：2008-09-04
申请号：US12012386
申请日：2008-02-01
申请人： Adrian Barbu , Le Lu , Luca Bogoni , Marcos Salganicoff , Dorin Comaniciu
发明人： Adrian Barbu , Le Lu , Luca Bogoni , Marcos Salganicoff , Dorin Comaniciu
IPC分类号： G06T17/00 , G06K9/00
CPC分类号： G06K9/32 , G06K9/00201 , G06K9/6256 , G06K2209/051
摘要： A method and system for detecting 3D objects in images is disclosed. In particular, a method and system for Ileo-Cecal Valve detection in 3D computed tomography (CT) images using incremental parameter learning and ICV specific prior learning is disclosed. First, second, and third classifiers are sequentially trained to detect candidates for position, scale, and orientation parameters of a box that bounds an object in 3D image. In the training of each sequential classifier, new training samples are generated by scanning the object's configuration parameters in the current learning projected subspace (position, scale, orientation), based on detected candidates resulting from the previous training step. This allows simultaneous detection and registration of a 3D object with full 9 degrees of freedom. ICV specific prior learning can be used to detect candidate voxels for an orifice of the ICV and to detect initial ICV box candidates using a constrained orientation alignment at each candidate voxel.
摘要翻译：公开了一种用于检测图像中的3D物体的方法和系统。特别地，公开了使用增量参数学习和ICV特有的先前学习的3D计算机断层摄影（CT）图像中Ileo-Cecal Valve检测的方法和系统。顺序训练第一，第二和第三分类器以检测在3D图像中界定对象的框的位置，缩放和取向参数的候选。在每个顺序分类器的训练中，基于从先前训练步骤产生的检测到的候选，通过在当前学习投影子空间（位置，比例，方向）中扫描对象的配置参数来生成新的训练样本。这允许同时检测和注册具有全9自由度的3D对象。 ICV具体的先验学习可用于检测ICV孔口的候选体素，并使用每个候选体素上的约束取向对齐来检测初始ICV盒候选。

13. 发明申请

US20060050960A1 System and method for anatomical structure parsing and detection 有权
标题翻译：用于解剖结构解析和检测的系统和方法
公开(公告)号：US20060050960A1
公开(公告)日：2006-03-09
申请号：US11219487
申请日：2005-09-02
申请人： Zhuowen Tu , Xiang Zhou , Dorin Comaniciu , Christian Schultz
发明人： Zhuowen Tu , Xiang Zhou , Dorin Comaniciu , Christian Schultz
IPC分类号： G06K9/34
CPC分类号： G06K9/6256 , G06T7/11 , G06T7/143 , G06T2207/10072 , G06T2207/30004
摘要： A method for segmenting a digitized image includes providing a digitized image comprising a plurality of intensities defined on a lattice of pixels, initializing a Bayesian prior probability and likelihood function for segmenting said image, decomposing said image into a union of a plurality of subspaces wherein each subspace is a product of k-partitions, and distributing a plurality Bayesian posterior probability functions over said subspaces, updating said Bayesian posterior probability functions via a data-driven Markov-chain, and selecting a subset of said Bayesian posterior probability functions that approximate a true Bayesian posterior probability function.
摘要翻译：用于分割数字化图像的方法包括提供包括在像素格子上定义的多个强度的数字化图像，初始化贝叶斯先验概率和用于分割所述图像的似然函数，将所述图像分解成多个子空间的并集，其中每个子空间子空间是k分区的乘积，并且在所述子空间上分布多个贝叶斯后验概率函数，经由数据驱动马尔科夫链更新所述贝叶斯后验概率函数，并且选择近似真实的所述贝叶斯后验概率函数的子集贝叶斯后验概率函数。

14. 发明授权

US07447359B2 System and method for anatomical structure parsing and detection 有权
标题翻译：用于解剖结构解析和检测的系统和方法
公开(公告)号：US07447359B2
公开(公告)日：2008-11-04
申请号：US11219487
申请日：2005-09-02
申请人： Zhuowen Tu , Xiang Zhou , Dorin Comaniciu , Christian P. Schultz
发明人： Zhuowen Tu , Xiang Zhou , Dorin Comaniciu , Christian P. Schultz
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06T7/11 , G06T7/143 , G06T2207/10072 , G06T2207/30004
摘要： A method for segmenting a digitized image includes providing a digitized image comprising a plurality of intensities defined on a lattice of pixels, initializing a Bayesian prior probability and likelihood function for segmenting said image, decomposing said image into a union of a plurality of subspaces wherein each subspace is a product of k-partitions, and distributing a plurality Bayesian posterior probability functions over said subspaces, updating said Bayesian posterior probability functions via a data-driven Markov-chain, and selecting a subset of said Bayesian posterior probability functions that approximate a true Bayesian posterior probability function.
摘要翻译：用于分割数字化图像的方法包括提供包括在像素格子上定义的多个强度的数字化图像，初始化贝叶斯先验概率和用于分割所述图像的似然函数，将所述图像分解成多个子空间的并集，其中每个子空间子空间是k分区的乘积，并且在所述子空间上分布多个贝叶斯后验概率函数，经由数据驱动马尔科夫链更新所述贝叶斯后验概率函数，并且选择近似真实的所述贝叶斯后验概率函数的子集贝叶斯后验概率函数。

15. 发明申请

US20070130206A1 System and Method For Integrating Heterogeneous Biomedical Information 审中-公开
标题翻译：用于整合异构生物医学信息的系统和方法
公开(公告)号：US20070130206A1
公开(公告)日：2007-06-07
申请号：US11462616
申请日：2006-08-04
申请人： Xiang Zhou , Dorin Comaniciu , Alok Gupta , Zhuowen Tu , Daniel Fasulo , Lu-yong Wang , Peiya Liu , Saikat Mukherjee , Amit Chakraborty
发明人： Xiang Zhou , Dorin Comaniciu , Alok Gupta , Zhuowen Tu , Daniel Fasulo , Lu-yong Wang , Peiya Liu , Saikat Mukherjee , Amit Chakraborty
IPC分类号： G06F17/00
CPC分类号： G16H50/20 , G16H10/60 , G16H50/50 , G16H50/70
摘要： A system and method for using heterogeneous data from multiple healthcare information sources in a medical decision support system is disclosed. Each healthcare information system stores medical data using a different local schema. The medical decision support system provides responses to user queries. A query is received from a user that is generated in a standardized global schema. The query includes information from medical ontologies. Database queries are generated from the user queries that use the medical ontologies to generate constraints in the queries. The medical ontologies are also used to infer database queries. The generated query is translated into multiple queries for the multiple healthcare systems wherein each query is in the local schema of the healthcare information system that is being queried. Each database query is transmitted to one of the healthcare information systems based on the local schema of the particular query. Data is collected from each of the queried healthcare information system and analyzed. A query response is formulated for the user
摘要翻译：公开了一种在医疗决策支持系统中使用来自多个保健信息源的异构数据的系统和方法。每个保健信息系统使用不同的本地模式存储医疗数据。医疗决策支持系统提供对用户查询的响应。从标准化全局模式中生成的用户接收到查询。该查询包括医学本体的信息。数据库查询是从使用医学本体的用户查询生成查询中的约束生成的。医学本体也用于推断数据库查询。所生成的查询被转换成多个医疗保健系统的多个查询，其中每个查询在被查询的医疗保健信息系统的本地模式中。基于特定查询的本地模式，将每个数据库查询发送到医疗保健信息系统之一。从每个被查询的医疗信息系统收集数据并进行分析。为用户制定了查询响应

16. 发明申请

US20060008138A1 System and method for 3D contour tracking of anatomical structures 有权
标题翻译：解剖结构3D轮廓跟踪的系统和方法
公开(公告)号：US20060008138A1
公开(公告)日：2006-01-12
申请号：US11154349
申请日：2005-06-16
申请人： Xiang Zhou , Dorin Comaniciu , Luca Bogoni
发明人： Xiang Zhou , Dorin Comaniciu , Luca Bogoni
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/3216 , G06K9/6209 , G06K2209/051 , G06T7/11 , G06T7/12 , G06T2207/10072 , G06T2207/20081 , G06T2207/30028
摘要： A method for three-dimensional contour tracking includes building a plurality of shape models, building a plurality of appearance models, training a learning/classification algorithm using said shape models and appearance models, localizing a contour using the said learning/classification algorithm about an object in a digitized image, and tracking said contour along said object in 3D incorporating said learning/classification algorithm.
摘要翻译：一种用于三维轮廓跟踪的方法包括建立多个形状模型，构建多个外观模型，使用所述形状模型和外观模型训练学习/分类算法，使用关于对象的所述学习/分类算法来定位轮廓在数字化图像中，并且沿3D结合所述学习/分类算法的3D跟踪所述轮廓。

17. 发明授权

US07664328B2 Joint classification and subtype discovery in tumor diagnosis by gene expression profiling 有权
标题翻译：通过基因表达谱分析肿瘤诊断中的联合分类和亚型发现
公开(公告)号：US07664328B2
公开(公告)日：2010-02-16
申请号：US11424135
申请日：2006-06-14
申请人： Lu-yong Wang , Zhuowen Tu , Daniel Fasulo , Dorin Comaniciu
发明人： Lu-yong Wang , Zhuowen Tu , Daniel Fasulo , Dorin Comaniciu
IPC分类号： G06K9/62
CPC分类号： G06K9/6256 , G06K9/00147
摘要： A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.
摘要翻译：程序存储装置由机器可读，有形地体现了可由机器执行的指令程序，以执行基于基因表达谱分类生物组织的方法步骤。方法步骤包括提供已知组织样本的基因表达谱的训练集，通过使用加强组合弱分类器提供已知组织样本的第一层强分类器，基于第一分类器创建两个样本集，填充两个样本基于上一层分类器设置下一层分类器，以树数据结构组织分类器，并输出树数据结构作为用于组织样本分类和疾病亚型发现的概率增强树分类器。通过找到最优特征并将多类问题划分为两类，将多类诊断问题转化为两类诊断过程。

18. 发明申请

US20070133857A1 JOINT CLASSIFICATION AND SUBTYPE DISCOVERY IN TUMOR DIAGNOSIS BY GENE EXPRESSION PROFILING 有权
标题翻译：通过基因表达分析对肿瘤诊断中的联合分类和亚型发现
公开(公告)号：US20070133857A1
公开(公告)日：2007-06-14
申请号：US11424135
申请日：2006-06-14
申请人： Lu-yong Wang , Zhuowen Tu , Daniel Fasulo , Dorin Comaniciu
发明人： Lu-yong Wang , Zhuowen Tu , Daniel Fasulo , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/00147
摘要： A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.
摘要翻译：程序存储装置由机器可读，有形地体现了可由机器执行的指令程序，以执行基于基因表达谱分类生物组织的方法步骤。方法步骤包括提供已知组织样本的基因表达谱的训练集，通过使用加强组合弱分类器提供已知组织样本的第一层强分类器，基于第一分类器创建两个样本集，填充两个样本基于上一层分类器设置下一层分类器，以树数据结构组织分类器，并输出树数据结构作为用于组织样本分类和疾病亚型发现的概率增强树分类器。通过找到最优特征并将多类问题划分为两类，将多类诊断问题转化为两类诊断过程。

19. 发明授权

US07894647B2 System and method for 3D contour tracking of anatomical structures 有权
标题翻译：解剖结构3D轮廓跟踪的系统和方法
公开(公告)号：US07894647B2
公开(公告)日：2011-02-22
申请号：US11154349
申请日：2005-06-16
申请人： Xiang Sean Zhou , Dorin Comaniciu , Luca Bogoni
发明人： Xiang Sean Zhou , Dorin Comaniciu , Luca Bogoni
IPC分类号： G06K9/00
CPC分类号： G06K9/3216 , G06K9/6209 , G06K2209/051 , G06T7/11 , G06T7/12 , G06T2207/10072 , G06T2207/20081 , G06T2207/30028
摘要： A method for three-dimensional contour tracking includes building a plurality of shape models, building a plurality of appearance models, training a learning/classification algorithm using said shape models and appearance models, localizing a contour using the said learning/classification algorithm about an object in a digitized image, and tracking said contour along said object in 3D incorporating said learning/classification algorithm.
摘要翻译：一种用于三维轮廓跟踪的方法包括建立多个形状模型，构建多个外观模型，使用所述形状模型和外观模型训练学习/分类算法，使用关于对象的所述学习/分类算法来定位轮廓在数字化图像中，并且沿3D结合所述学习/分类算法的3D跟踪所述轮廓。

20. 发明授权

US07308132B2 Method for robust scale-space analysis of 3D local structures in medical images 有权
标题翻译：用于医学图像中3D局部结构的鲁棒尺度空间分析方法
公开(公告)号：US07308132B2
公开(公告)日：2007-12-11
申请号：US10892646
申请日：2004-07-16
申请人： Kazunori Okada , Dorin Comaniciu , Luca Bogoni
发明人： Kazunori Okada , Dorin Comaniciu , Luca Bogoni
IPC分类号： G06K9/00
CPC分类号： G06K9/4671 , G06T7/60 , G06T7/70 , G06T2200/04 , G06T2207/10072 , G06T2207/20016 , G06T2207/20101 , G06T2207/30096
摘要： A method for determining the location, shape and orientation of a tumor in a medical image includes finding a plurality of spatial extrema μ of a D-dimensional spatial signal f for a set of bandwidths H by performing mean shift-based gradient-ascent iterations for a set of bandwidths H and then determining a D-dimensional spread and orientation of the signal about each extrema μ by estimating a covariance Σ of the signal f for each extrema μ. The optimal estimate of μ and Σ is determined by performing a Jensen-Shannon divergence on the full set of μ and Σ.
摘要翻译：用于确定医学图像中的肿瘤的位置，形状和取向的方法包括：通过执行基于平均移位的梯度上升迭代来为一组带宽H找到D维空间信号f的多个空间极值μ 一组带宽H，然后通过估计每个极值mu的信号f的协方差Sigma来确定关于每个极值mu的信号的D维扩展和取向。通过在全套mu和Sigma上执行Jensen-Shannon分歧来确定mu和Sigma的最佳估计。

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