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    • 2. 发明申请
    • Analyzing pixel data by imprinting objects of a computer-implemented network structure into other objects
    • 通过将计算机实现的网络结构的对象压印到其他对象中来分析像素数据
    • US20100265267A1
    • 2010-10-21
    • US12386380
    • 2009-04-17
    • Arno SchaepeGuenter SchmidtOwen FeehanGerd Binnig
    • Arno SchaepeGuenter SchmidtOwen FeehanGerd Binnig
    • G06K9/62G06K9/00G09G5/00G06F7/00
    • G06K9/00637G06K9/00657G06K9/469
    • An analysis system analyzes digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers and object networks. Objects in a first object network are segmented into a first class, and objects in a second object network are segmented into a second class. One process step of the process hierarchy involves generating a third object network by imprinting objects of the first object network into the objects of the second object network such that pixel locations are unlinked from objects of the second object network to the extent that the pixel locations were also linked to objects of the first object network. The imprinting step allows object-oriented processing of digital images to be performed with fewer computations and less memory. Characteristics of an object of the third object network are then determined by measuring the object.
    • 分析系统使用包括过程层次,类网络和数据网络的计算机实现的网络结构来分析数字图像。 数据网络包括图像层和对象网络。 第一对象网络中的对象被分割成第一类,并且第二对象网络中的对象被分割成第二类。 过程层级的一个过程步骤包括通过将第一对象网络的对象压印到第二对象网络的对象中来生成第三对象网络,使得像素位置与第二对象网络的对象脱离到像素位置是 也链接到第一对象网络的对象。 压印步骤允许以更少的计算和更少的存储器执行数字图像的面向对象处理。 然后通过测量对象来确定第三对象网络的对象的​​特征。
    • 3. 发明申请
    • Context driven image mining to generate image-based biomarkers
    • 上下文驱动的图像挖掘生成基于图像的生物标志物
    • US20110122138A1
    • 2011-05-26
    • US12930873
    • 2011-01-18
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • G06T11/20G06K9/00
    • G06T7/0014G01N2800/00G06K9/6253G06T7/0012G06T2207/10116G06T2207/30004G06T2207/30061G06T2207/30068
    • An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.
    • 使用通过医学图像的面向对象图像分析获得的图像特征来生成基于图像的生物标志物。 测量和加权图像特征的第一子集的值。 将图像特征的加权值相加以计算第一基于图像的生物标志物的大小。 每个患者的生物标志物的大小与对其医学图像分析的患者观察到的临床终点相关,例如存活时间。 相关性作为散点图显示在图形用户界面上。 选择属于第二基于图像的生物标志物的图像特征的第二子集,使得用于患者的第二基于图像的生物标志物的量级与对于那些患者观察到的临床终点更为相关。 然后可以将第二种生物标志物用于预测尚未观察到其临床终点的其他患者的临床终点。
    • 4. 发明授权
    • Analyzing pixel data by imprinting objects of a computer-implemented network structure into other objects
    • 通过将计算机实现的网络结构的对象压印到其他对象中来分析像素数据
    • US08319793B2
    • 2012-11-27
    • US12386380
    • 2009-04-17
    • Arno SchaepeGuenter SchmidtOwen FeehanGerd Binnig
    • Arno SchaepeGuenter SchmidtOwen FeehanGerd Binnig
    • G09G5/14
    • G06K9/00637G06K9/00657G06K9/469
    • An analysis system analyzes digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers and object networks. Objects in a first object network are segmented into a first class, and objects in a second object network are segmented into a second class. One process step of the process hierarchy involves generating a third object network by imprinting objects of the first object network into the objects of the second object network such that pixel locations are unlinked from objects of the second object network to the extent that the pixel locations were also linked to objects of the first object network. The imprinting step allows object-oriented processing of digital images to be performed with fewer computations and less memory. Characteristics of an object of the third object network are then determined by measuring the object.
    • 分析系统使用包括过程层次,类网络和数据网络的计算机实现的网络结构来分析数字图像。 数据网络包括图像层和对象网络。 第一对象网络中的对象被分割成第一类,并且第二对象网络中的对象被分割成第二类。 过程层级的一个过程步骤包括通过将第一对象网络的对象压印到第二对象网络的对象中来生成第三对象网络,使得像素位置与第二对象网络的对象脱离到像素位置是 也链接到第一对象网络的对象。 压印步骤允许以更少的计算和更少的存储器执行数字图像的面向对象处理。 然后通过测量对象来确定第三对象网络的对象的​​特征。
    • 6. 发明授权
    • Context driven image mining to generate image-based biomarkers
    • 上下文驱动的图像挖掘生成基于图像的生物标志物
    • US08594410B2
    • 2013-11-26
    • US12930873
    • 2011-01-18
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • G06K9/00G06K9/62
    • G06T7/0014G01N2800/00G06K9/6253G06T7/0012G06T2207/10116G06T2207/30004G06T2207/30061G06T2207/30068
    • An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.
    • 使用通过医学图像的面向对象图像分析获得的图像特征来生成基于图像的生物标志物。 测量和加权图像特征的第一子集的值。 将图像特征的加权值相加以计算第一基于图像的生物标志物的大小。 每个患者的生物标志物的大小与对其医学图像分析的患者观察到的临床终点相关,例如存活时间。 相关性作为散点图显示在图形用户界面上。 选择属于第二基于图像的生物标志物的图像特征的第二子集,使得用于患者的第二基于图像的生物标志物的量级与对于那些患者观察到的临床终点更为相关。 然后可以将第二种生物标志物用于预测尚未观察到其临床终点的其他患者的临床终点。
    • 8. 发明授权
    • Cognition integrator and language
    • 认知集成者和语言
    • US07873223B2
    • 2011-01-18
    • US11511930
    • 2006-08-28
    • Gerd BinnigGuenter SchmidtArno Schaepe
    • Gerd BinnigGuenter SchmidtArno Schaepe
    • G06K9/62
    • G06K9/6253G06K9/00127G06K9/0014G06K9/468G06K9/6282G06N5/022
    • In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.
    • 在规范模式中,用户使用新颖的脚本语言来指定类网络的类和处理层次的处理步骤。 这些课程描述了用户期望在数字图像中找到什么。 流程层次描述了数字图像的分析方式。 每个处理步骤包括一个算法和一个域,它指定算法要运行的类。 认知程序获取包括数字图像的像素值的表格数据以及与数字图像有关的元数据。 在执行模式下,认知程序生成数据网络,其中像素值链接到对象,对象被分类为属于类。 过程步骤,类和对象以计算机实现的网络结构彼此链接,使得认知程序能够检测数字图像中的目标对象。
    • 9. 发明申请
    • Cognition integrator and language
    • 认知集成者和语言
    • US20070122017A1
    • 2007-05-31
    • US11511930
    • 2006-08-28
    • Gerd BinnigGuenter SchmidtArno Schaepe
    • Gerd BinnigGuenter SchmidtArno Schaepe
    • G06K9/00G06K9/62
    • G06K9/6253G06K9/00127G06K9/0014G06K9/468G06K9/6282G06N5/022
    • In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.
    • 在规范模式中,用户使用新颖的脚本语言来指定类网络的类和处理层次的处理步骤。 这些课程描述了用户期望在数字图像中找到什么。 流程层次描述了数字图像的分析方式。 每个处理步骤包括一个算法和一个域,它指定算法要运行的类。 认知程序获取包括数字图像的像素值的表格数据以及与数字图像有关的元数据。 在执行模式下,认知程序生成数据网络,其中像素值链接到对象,对象被分类为属于类。 过程步骤,类和对象以计算机实现的网络结构彼此链接,使得认知程序能够检测数字图像中的目标对象。