-
1.发明授权公开(公告)号：US07729739B2
公开(公告)日：2010-06-01
申请号：US11000515
申请日：2004-11-29
IPC分类号： A61B6/00
CPC分类号： G06T7/0012 , G06T7/136 , G06T7/64 , G06T7/68 , G06T2207/20152 , G06T2207/30032
摘要： A method for detecting and identifying structures of interest such as colonic polyps or similar structures like lung nodules in volumetric (medical) images data is provided. The method includes obtaining a heat diffusion field (HDF) by applying a heat diffusion scheme to a volume of interest that includes structures. The obtained heat diffusion field is then used for identifying a structure of interest from the structures in the volume of interest using a geometrical analysis of the heat diffusion field. The heat diffusion scheme is, at least partly, governed by non-linear diffusion parameters. The identification includes two parts: (i) the computation of a spherical symmetry parameter, and (ii) the performance of a local analysis of the volume of interest and computation of a triangulization parameter.
摘要翻译： 提供了一种用于在体积（医学）图像数据中检测和识别兴趣结构如结肠息肉或类似结节如肺结节的方法。 该方法包括通过将热扩散方案应用到包括结构的感兴趣体积来获得热扩散场（HDF）。 然后使用所述热扩散场的几何分析，将所获得的热扩散场用于从感兴趣体积中的结构中鉴定感兴趣的结构。 热扩散方案至少部分地由非线性扩散参数控制。 识别包括两部分：（i）球面对称参数的计算，以及（ii）感兴趣体积的局部分析和三角化参数的计算的性能。
-
2.发明申请公开(公告)号：US20050149286A1
公开(公告)日：2005-07-07
申请号：US11000515
申请日：2004-11-29
申请人： Burak Acar , Ender Konukoglu , Christopher Beaulieu , Sandy Napel , David Paik
发明人： Burak Acar , Ender Konukoglu , Christopher Beaulieu , Sandy Napel , David Paik
CPC分类号： G06T7/0012 , G06T7/136 , G06T7/64 , G06T7/68 , G06T2207/20152 , G06T2207/30032
摘要： A method for detecting and identifying structures of interest such as colonic polyps or similar structures like lung nodules in volumetric (medical) images data is provided. The method includes obtaining a heat diffusion field (HDF) by applying a heat diffusion scheme to a volume of interest that includes structures. The obtained heat diffusion field is then used for identifying a structure of interest from the structures in the volume of interest using a geometrical analysis of the heat diffusion field. The heat diffusion scheme is, at least partly, governed by non-linear diffusion parameters. The identification includes two parts: (i) the computation of a spherical symmetry parameter, and (ii) the performance of a local analysis of the volume of interest and computation of a triangulization parameter.
摘要翻译： 提供了一种用于在体积（医学）图像数据中检测和识别兴趣结构如结肠息肉或类似结节如肺结节的方法。 该方法包括通过将热扩散方案应用到包括结构的感兴趣体积来获得热扩散场（HDF）。 然后使用所述热扩散场的几何分析，将所获得的热扩散场用于从感兴趣体积中的结构中鉴定感兴趣的结构。 热扩散方案至少部分地由非线性扩散参数控制。 识别包括两部分：（i）球面对称参数的计算，以及（ii）感兴趣体积的局部分析和三角化参数的计算的性能。
-
公开(公告)号：US20120166462A1
公开(公告)日：2012-06-28
申请号：US12979362
申请日：2010-12-28
申请人： Sayan D. Pathak , Antonio Criminisi , Steven J. White , Liqun Fu , Khan M. Siddiqui , Toby Sharp , Ender Konukoglu , Bryan Dove , Michael T. Gillam
发明人： Sayan D. Pathak , Antonio Criminisi , Steven J. White , Liqun Fu , Khan M. Siddiqui , Toby Sharp , Ender Konukoglu , Bryan Dove , Michael T. Gillam
CPC分类号： G06F3/04845 , G06F3/04842 , G06F9/451 , G06F19/321 , G16H15/00 , G16H40/63
摘要： The present discussion relates to automated image data processing and visualization. One example can facilitate generating a graphical user-interface (GUI) from image data that includes multiple semantically-labeled user-selectable anatomical structures. This example can receive a user selection of an individual semantically-labeled user-selectable anatomical structure. The example can locate a sub-set of the image data associated with the individual semantically-labeled user-selectable anatomical structure and can cause presentation of the sub-set of the image data on a subsequent GUI.
摘要翻译： 本发明涉及自动图像数据处理和可视化。 一个示例可以有助于从包括多个语义标记的用户可选解剖结构的图像数据生成图形用户界面（GUI）。 该示例可以接收用户选择单个语义标记的用户可选择的解剖结构。 该示例可以定位与单独的语义标记的用户可选择的解剖结构相关联的图像数据的子集，并且可以在随后的GUI上引起图像数据的子集的呈现。
-
公开(公告)号：US09710730B2
公开(公告)日：2017-07-18
申请号：US13025500
申请日：2011-02-11
申请人： Ender Konukoglu , Sayan Pathak , Khan Mohammad Siddiqui , Antonio Criminisi , Steven White , Jamie Daniel Joseph Shotton , Duncan Paul Robertson
发明人： Ender Konukoglu , Sayan Pathak , Khan Mohammad Siddiqui , Antonio Criminisi , Steven White , Jamie Daniel Joseph Shotton , Duncan Paul Robertson
CPC分类号： G06K9/6277 , G06K9/6282 , G06T7/35 , G06T2207/10072 , G06T2207/10116 , G06T2207/10132 , G06T2207/20072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30004
摘要： Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.
-
公开(公告)号：US08954365B2
公开(公告)日：2015-02-10
申请号：US13528866
申请日：2012-06-21
CPC分类号： G06K9/6232 , G06K9/6219 , G06K9/6226 , G06K9/6252
摘要： Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.
摘要翻译： 例如，对于计算机视觉，医学图像分析，文本文档聚类来描述密度估计和/或歧管学习。 在各种实施例中，使用未标记的数据来训练密度森林以估计数据分布。 在实施例中，密度森林包括多个随机决策树，每个随机决策树将训练数据的部分在其叶片上聚集成簇。 在实施例中，表示每个树上的聚类的概率分布被聚合以形成森林密度，森林密度是可以从其生成未标记数据的概率密度函数的估计。 映射引擎可以使用密度森林叶片处的簇来估计将未标记数据映射到较低维空间的映射函数，同时保留未标记数据点之间的相对距离或其他关系。 采样引擎可以使用密度森林来从森林密度随机抽取数据。
-
公开(公告)号：US20130343619A1
公开(公告)日：2013-12-26
申请号：US13528866
申请日：2012-06-21
IPC分类号： G06K9/62
CPC分类号： G06K9/6232 , G06K9/6219 , G06K9/6226 , G06K9/6252
摘要： Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.
摘要翻译： 例如，对于计算机视觉，医学图像分析，文本文档聚类来描述密度估计和/或歧管学习。 在各种实施例中，使用未标记的数据来训练密度森林以估计数据分布。 在实施例中，密度森林包括多个随机决策树，每个随机决策树将训练数据的部分在其叶片上累积成簇。 在实施例中，表示每个树上的聚类的概率分布被聚合以形成森林密度，森林密度是可以从其生成未标记数据的概率密度函数的估计。 映射引擎可以使用密度森林叶片处的簇来估计将未标记数据映射到较低维空间的映射函数，同时保留未标记数据点之间的相对距离或其他关系。 采样引擎可以使用密度森林来从森林密度随机抽取数据。
-
公开(公告)号：US20120207359A1
公开(公告)日：2012-08-16
申请号：US13025500
申请日：2011-02-11
申请人： Ender Konukoglu , Sayan Pathak , Khan Mohammad Siddiqui , Antonio Criminisi , Steven White , Jamie Daniel Joseph Shotton , Duncan Paul Robertson
发明人： Ender Konukoglu , Sayan Pathak , Khan Mohammad Siddiqui , Antonio Criminisi , Steven White , Jamie Daniel Joseph Shotton , Duncan Paul Robertson
CPC分类号： G06K9/6277 , G06K9/6282 , G06T7/35 , G06T2207/10072 , G06T2207/10116 , G06T2207/10132 , G06T2207/20072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30004
摘要： Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.
摘要翻译： 描述图像注册。 在一个实施例中，图像注册系统执行图像的自动注册，例如医学图像。 在一个示例中，为要注册的每个图像计算语义信息，包括关于图像中的对象的类型和该信息的确定性的信息。 在一个示例中，发现映射以以该语义信息的确定性加权的方式注册考虑了图像元素的强度以及语义信息的图像。 例如，通过使用回归森林估计解剖结构的位置的后验分布并将后验分布变换为概率图来计算语义信息。 在一个示例中，映射被发现为能量函数的拐点的全局点，能量函数具有与语义信息相关的术语。
-
-
-
-
-
-
搜索结果
7 条记录 (耗时 0.011 秒)