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1. 发明申请

US20060064017A1 Hierarchical medical image view determination 审中-公开
标题翻译：分层医学图像视图确定
公开(公告)号：US20060064017A1
公开(公告)日：2006-03-23
申请号：US11231593
申请日：2005-09-21
申请人： Sriram Krishnan , Jinbo Bi , R. Rao , Jonathan Stoeckel , Matthew Otey
发明人： Sriram Krishnan , Jinbo Bi , R. Rao , Jonathan Stoeckel , Matthew Otey
IPC分类号： A61B8/02
CPC分类号： G06T7/0012 , G06K9/6217 , G06K9/6282 , G06T2207/30048
摘要： A cardiac view of a medical ultrasound image is automatically identified. By grouping different views into sub-categories, a hierarchal classifier identifies the views. For example, apical views are distinguished from parasternal views. Specific types of apical or parasternal views are identified based on distinguishing between images of the geneses. Different features are used for classifying, such as gradients, functions of the gradients, statistics of an average frame of data from a clip or sequence of frames, or a number of edges along a given direction. The number of features used may be compressed, such as by classifying a plurality of features into a new feature. For example, alpha weights in a model of features and classes are determined and used as features for classification.
摘要翻译：医疗超声图像的心脏视图被自动识别。通过将不同的视图分组到子类别中，层级分类器标识视图。例如，顶端视图与胸骨旁观点不同。基于区分基因图像来确定顶端或胸骨旁观察的具体类型。不同的特征用于分类，例如梯度，梯度的功能，来自剪辑或帧序列的平均数据帧的统计，或沿给定方向的多个边缘的统计。所使用的特征的数量可以被压缩，例如通过将多个特征分类为新特征。例如，特征和类的模型中的α权重被确定并用作分类的特征。

2. 发明申请

US20070011121A1 System and method for learning rankings via convex hull separation 审中-公开
公开(公告)号：US20070011121A1
公开(公告)日：2007-01-11
申请号：US11444606
申请日：2006-06-01
申请人： Jinbo Bi , Glenn Fung , Sriram Krishnan , Balaji Krishnapuram , R. Rao , Romer Rosales
发明人： Jinbo Bi , Glenn Fung , Sriram Krishnan , Balaji Krishnapuram , R. Rao , Romer Rosales
IPC分类号： G06F15/18
CPC分类号： G06K9/6269
摘要： A method for finding a ranking function ƒ that classifies feature points in an n-dimensional space includes providing a plurality of feature points xk derived from tissue sample regions in a digital medical image, providing training data A comprising training samples Aj where A = ⋃ j = 1 S ⁢ ( A j = { x i j } i = 1 m j ) , providing an ordering E={(P,Q)|APAQ} of at least some training data sets where all training samples xiεAP are ranked higher than any sample xjεAQ, solving a mathematical optimization program to determine the ranking function ƒ that classifies said feature points x into sets A. For any two sets Ai, Aj, AiAj, and the ranking function ƒ satisfies inequality constraints ƒ(xi)≦ƒ(xj) for all xiεconv(Ai) and xjεconv(Aj), where conv(A) represents the convex hull of the elements of set A.

3. 发明申请

US20050209519A1 Hierarchical modeling in medical abnormality detection 有权
标题翻译：医学异常检测中的分层建模
公开(公告)号：US20050209519A1
公开(公告)日：2005-09-22
申请号：US11054600
申请日：2005-02-08
申请人： Sriram Krishnan , Jinbo Bi , R. Rao
发明人： Sriram Krishnan , Jinbo Bi , R. Rao
IPC分类号： G06F19/00 , A61B8/00
CPC分类号： G16H50/20 , G06F19/00 , G16H50/50
摘要： Hierarchal modeling is used to distinguish one state or class from three or more classes. In a first stage, a normal or other class is distinguished from a diseased or other groups of classes. If the results of the first stage classification indicate diseased or data within the groups of different classes, a subsequent stage of classification is performed. In a subsequent stage of classification, the data is classified to distinguish one or more other classes from the remaining classes. Using two or more stages, medical information is classified by eliminating one or more possible classes in each stage to finally identify a particular class most appropriate or probable for the data.
摘要翻译：层次建模用于将一个状态或类与三个或更多个类进行区分。在第一阶段，正常或其他类别与患病或其他类别的群体不同。如果第一阶段分类的结果表示不同类别的组内的患病或数据，则执行后续分类阶段。在分类的后续阶段，将数据分类为将一个或多个其他类别与其余类别区分开。使用两个或更多个阶段，通过消除每个阶段中的一个或多个可能的类别来最终识别数据最适合或可能的特定类别来分类医学信息。

4. 发明申请

US20050177040A1 System and method for an iterative technique to determine fisher discriminant using heterogenous kernels 审中-公开
标题翻译：用于使用异质内核确定渔夫判别式的迭代技术的系统和方法
公开(公告)号：US20050177040A1
公开(公告)日：2005-08-11
申请号：US11050599
申请日：2005-02-03
申请人： Glenn Fung , Murat Dundar , Jinbo Bi , R. Rao
发明人： Glenn Fung , Murat Dundar , Jinbo Bi , R. Rao
IPC分类号： G06K9/62 , G06T7/00 , A61B5/05
CPC分类号： G06K9/6234 , G06T7/0012
摘要： A method and device with instructions for analyzing an image data-space includes creating a library of one or more kernels, wherein each kernel from the library of the kernels maps the image data-space to a first data-space using at least one mapping function; and learning a linear combination of kernels in an automatic manner to generate at least one of a classifier and a regressor which is applied to the first data-space. The linear combination of kernels is used to generate a classified image-data space to detect at least one of the candidates in the classified image-data space.
摘要翻译：一种具有用于分析图像数据空间的指令的方法和装置包括创建一个或多个内核的库，其中来自该库的库中的每个内核使用至少一个映射函数将图像数据空间映射到第一数据空间 ; 以及以自动方式学习内核的线性组合，以生成应用于所述第一数据空间的分类器和回归器中的至少一个。使用内核的线性组合来生成分类图像数据空间，以检测分类图像数据空间中的候选者中的至少一个。

5. 发明申请

US20070110292A1 System and method for computer aided detection via asymmetric cascade of sparse linear classifiers 有权
标题翻译：通过稀疏线性分类器的不对称级联进行计算机辅助检测的系统和方法
公开(公告)号：US20070110292A1
公开(公告)日：2007-05-17
申请号：US11592869
申请日：2006-11-03
申请人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Rao
发明人： Jinbo Bi , Senthil Periaswamy , Kazunori Okada , Toshiro Kubota , Glenn Fung , Marcos Salganicoff , R. Rao
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/6282
摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.
摘要翻译：一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选和特征，并且使用形式符号（w）的线性分类器的分级级联将所述异常候选分类为真阳性或假阳性 x + b），其中x是特征向量，w是加权向量，b是模型参数，其中不同的权重用于惩罚假否定和假肯定，并且其中更复杂的特征用于分级器级联的每个连续阶段。

6. 发明申请

US20050197980A1 System and method for a sparse kernel expansion for a bayes classifier 失效
标题翻译：用于Bayes分类器的稀疏内核扩展的系统和方法
公开(公告)号：US20050197980A1
公开(公告)日：2005-09-08
申请号：US11049187
申请日：2005-02-02
申请人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Rao
发明人： Murat Dundar , Glenn Fung , Jinbo Bi , R. Rao
IPC分类号： G06K9/62 , G06E1/00
CPC分类号： G06K9/6256
摘要： A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.
摘要翻译：具有用于通过学习分类器分析输入数据空间的指令的方法和设备包括从用于分析输入数据空间的预定训练数据集中选择候选子集。将候选者从候选子集临时添加到扩展集合，以通过对添加到扩展集合的候选者的一对一错误进行预先重复的评估来为输入数据空间生成新的内核空间。之后，在执行一次性错误评估之后，删除临时添加到扩展集的候选者，并且基于临时的候选者的一次性错误选择要永久添加到扩展集的候选项添加到扩展集以确定一个或多个分类器。

7. 发明申请

US20050059876A1 Systems and methods for providing automated regional myocardial assessment for cardiac imaging 有权
标题翻译：为心脏成像提供自动区域心肌评估的系统和方法
公开(公告)号：US20050059876A1
公开(公告)日：2005-03-17
申请号：US10876803
申请日：2004-06-25
申请人： Sriram Krishnan , R. Rao , Richard Bennett
发明人： Sriram Krishnan , R. Rao , Richard Bennett
IPC分类号： A61B5/05 , A61B8/00 , G06T7/00
CPC分类号： G06T7/0012 , A61B6/507 , G06F19/00 , G06T2207/30048
摘要： Systems and methods are provided for automated assessment of regional myocardial function using wall motion analysis methods that analyze various features/parameters of patient information (image data and non-image data) obtained from medical records of a patient. For example, a method for providing automatic diagnostic support for cardiac imaging generally comprises obtaining image data of a heart of a patient, obtaining features from the image data of the heart, which are related to motion of the myocardium of the heart, and automatically assessing regional myocardial function of one or more regions of a myocardial wall using the obtained features.
摘要翻译：提供了系统和方法，用于使用分析从患者的病历获得的患者信息（图像数据和非图像数据）的各种特征/参数的壁运动分析方法来自动评估区域心肌功能。例如，提供用于心脏成像的自动诊断支持的方法通常包括获得患者心脏的图像数据，从心脏的图像数据获得与心脏心肌的运动相关的特征，并自动评估使用获得的特征，心肌壁的一个或多个区域的区域心肌功能。

8. 发明申请

US20050020903A1 Systems and methods for automated diagnosis and decision support for heart related diseases and conditions 有权
标题翻译：用于心脏相关疾病和病症的自动诊断和决策支持的系统和方法
公开(公告)号：US20050020903A1
公开(公告)日：2005-01-27
申请号：US10876801
申请日：2004-06-25
申请人： Sriram Krishnan , Alok Gupta , R. Rao , Dorin Comaniciu , Xiang Zhou
发明人： Sriram Krishnan , Alok Gupta , R. Rao , Dorin Comaniciu , Xiang Zhou
IPC分类号： A61B5/05 , G06F17/00
CPC分类号： G16H50/20 , G06F19/00
摘要： CAD (computer-aided diagnosis) systems and applications for cardiac imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated assessment of regional myocardial function through wall motion analysis, automated diagnosis of heart diseases and conditions such as cardiomyopathy, coronary artery disease and other heart-related medical conditions, and other automated decision support functions. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.
摘要翻译：提供了用于心脏成像的CAD（计算机辅助诊断）系统和应用，其实现了从受试患者的患者信息（包括图像数据和/或非图像数据）的集合中自动提取和分析特征的方法，以提供对医师工作流程的各个方面的决策支持，包括例如通过壁运动分析自动评估区域心肌功能，心脏疾病的自动诊断和诸如心肌病，冠状动脉疾病和其他与心脏相关的医疗状况等条件，以及其他自动化决策支持功能。 CAD系统实施机器学习技术，其使用从一个或多个相关临床领域的标记的患者病例的数据库获得（学习）的一组训练数据和/或对这些数据的专家解释，使得CAD系统能够“学习” 分析患者数据，进行适当的诊断评估和决策，以协助医师的工作流程。

9. 发明申请

US20060239527A1 Three-dimensional cardiac border delineation in medical imaging 审中-公开
标题翻译：医学影像三维心脏边界描绘
公开(公告)号：US20060239527A1
公开(公告)日：2006-10-26
申请号：US11351060
申请日：2006-02-09
申请人： Sriram Krishnan , Dorin Comaniciu , Xiang Zhou , Bogdan Georgescu , Helene Houle , R. Rao
发明人： Sriram Krishnan , Dorin Comaniciu , Xiang Zhou , Bogdan Georgescu , Helene Houle , R. Rao
IPC分类号： G06K9/00
CPC分类号： A61B8/483 , A61B6/503 , A61B6/504 , A61B8/08 , A61B8/0883 , A61B8/0891
摘要： Three-dimensional cardiac border is delineated in medical imaging. A view is labeled, such as identifying a two-dimensional view as an apical four-chamber view. A three-dimensional border is detected as a function of the view label. For example, the view is associated from a plane through a volume and a known orientation relative to the heart. Labeling the view indicates the orientation of the heart in the scanned volume. By determining the orientation of the heart, border detection processes may be simplified or assisted.
摘要翻译：在医学成像中描绘了三维心脏边界。视图被标记，例如将二维视图识别为顶端四室视图。根据视图标签检测三维边框。例如，视图从平面通过体积和相对于心脏的已知取向相关联。标注视图表示心脏在扫描体积中的方向。通过确定心脏的方向，可以简化或辅助边界检测过程。

10. 发明申请

US20060200010A1 Guiding differential diagnosis through information maximization 有权
标题翻译：通过信息最大化指导鉴别诊断
公开(公告)号：US20060200010A1
公开(公告)日：2006-09-07
申请号：US11365271
申请日：2006-03-01
申请人： Romer Rosales , Marianne Mueller , Sriram Krishnan , R. Rao
发明人： Romer Rosales , Marianne Mueller , Sriram Krishnan , R. Rao
IPC分类号： A61B5/00
CPC分类号： G06F19/34 , G06F19/00 , G06F19/3481 , G06Q50/22 , G06Q50/24 , G16H15/00 , G16H50/20 , G16H50/70
摘要： A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.
摘要翻译：公开了一种基于信息最大化的诊断和处理决策的系统和方法。利用患者信息以及其他患者的临床记录，可以减少诊断和治疗方案的不确定性。信息最大化可以考虑额外的数据，如风险，成本和舒适度，做出适当的医疗决策。

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