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1.发明授权Global error minimization in image mosaicking using graph laplacians and its applications in microscopy 有权使用图形拉普拉斯算子的图像拼接中的全局误差最小化及其在显微镜中的应用公开(公告)号：US08983230B2
公开(公告)日：2015-03-17
申请号：US13810940
申请日：2011-07-25
申请人： Parmeshwar Khurd , Rafiou Oketokoun , Ali Kamen , Leo Grady , Hari Sundar , John V. Frangioni , Tejas Gajera
发明人： Parmeshwar Khurd , Rafiou Oketokoun , Ali Kamen , Leo Grady , Hari Sundar , John V. Frangioni , Tejas Gajera
CPC分类号： G06T11/60 , G06T3/0068 , G06T3/4038 , G06T7/32 , G06T2200/32 , G06T2207/10056 , G06T2207/30024
摘要： An image mosaicking method includes performing pairwise registration of a plurality of tiles (101), determining absolute homographies for each of the plurality of tiles according to the pairwise registration (102B), and performing a blending of the plurality of tiles to obtain a stitched image according to the absolute homographies (103).
摘要翻译： 图像拼接方法包括执行多个瓦片（101）的配对注册，根据成对配准（102B）确定多个瓦片中的每一个的绝对同位素，并且执行多个瓦片的混合以获得缝合图像 根据绝对同位素（103）。
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2.发明申请Global Error Minimization In Image Mosaicking Using Graph Laplacians And Its Applications In Microscopy 有权使用图形拉普拉斯算子的图像拼接中的全局误差最小化及其在显微镜中的应用公开(公告)号：US20130243352A1
公开(公告)日：2013-09-19
申请号：US13810940
申请日：2011-07-25
申请人： Parmeshwar Khurd , Rafiou Oketokoun , Ali Kamen , Leo Grady , Hari Sundar , John V. Frangioni
发明人： Parmeshwar Khurd , Rafiou Oketokoun , Ali Kamen , Leo Grady , Hari Sundar , John V. Frangioni
IPC分类号： G06T11/60
CPC分类号： G06T11/60 , G06T3/0068 , G06T3/4038 , G06T7/32 , G06T2200/32 , G06T2207/10056 , G06T2207/30024
摘要： An image mosaicking method includes performing pairwise registration of a plurality of tiles (101), determining absolute homographies for each of the plurality of tiles according to the pairwise registration (102B), and performing a blending of the plurality of tiles to obtain a stitched image according to the absolute homographies (103)
摘要翻译： 图像拼接方法包括执行多个瓦片（101）的配对注册，根据成对配准（102B）确定多个瓦片中的每一个的绝对同位素，并且执行多个瓦片的混合以获得缝合图像 根据绝对同位素（103）
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3.发明授权Systems and method for automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers 有权使用通过增强分类器初始化的随机步行器分割的MR图像中的自动前列腺定位的系统和方法公开(公告)号：US08811701B2
公开(公告)日：2014-08-19
申请号：US13291615
申请日：2011-11-08
申请人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
发明人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
CPC分类号： G06K9/6226 , G06T7/11 , G06T7/143 , G06T7/194 , G06T2207/10088 , G06T2207/20081 , G06T2207/20156 , G06T2207/30081
摘要： Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
摘要翻译： T2加权MR图像中的自动前列腺定位有利于劳动密集型癌症成像技术。 提供了准确分割MR图像前列腺的方法和系统，并解决了前列腺解剖和疾病，强度不均匀性以及直肠内线圈引起的假象的巨大变化。 用基于强度的多级高斯混合模型预期最大化（GMM-EM）原始MR图像分段训练的增强分类器自动检测前列腺的中心。 形状模型与多标签随机Walker（MLRW）结合使用，以限制MLRW内的播种过程。
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4.发明授权Method and system for integrated radiological and pathological information for diagnosis, therapy selection, and monitoring 有权用于诊断，治疗选择和监测的综合放射学和病理学信息的方法和系统公开(公告)号：US09478022B2
公开(公告)日：2016-10-25
申请号：US14240534
申请日：2012-08-22
申请人： Ali Kamen , Leo Grady , Gianluca Paladini , Parmeshwar Khurd , Oliver Kutter , Dorin Comaniciu
发明人： Ali Kamen , Leo Grady , Gianluca Paladini , Parmeshwar Khurd , Oliver Kutter , Dorin Comaniciu
CPC分类号： G06T7/0012 , A61B6/12 , A61B6/463 , A61B6/467 , A61B8/0841 , A61B8/463 , A61B8/465 , A61B8/467 , A61B10/02 , G06F19/00 , G06F19/3481 , G06T2207/30004 , G16H50/20
摘要： A method and system for integrating radiological and pathological information for cancer diagnosis, therapy selection, and monitoring is disclosed. A radiological image of a patient, such as a magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), or ultrasound image, is received. A location corresponding to each of one or more biopsy samples is determined in the at least one radiological image. An integrated display is used to display a histological image corresponding to the each biopsy samples, the radiological image, and the location corresponding to each biopsy samples in the radiological image. Pathological information and radiological information are integrated by combining features extracted from the histological images and the features extracted from the corresponding locations in the radiological image for cancer grading, prognosis prediction, and therapy selection.
摘要翻译： 公开了用于整合放射学和病理学信息用于癌症诊断，治疗选择和监测的方法和系统。 接收患者的放射学图像，例如磁共振（MR），计算机断层摄影（CT），正电子发射断层摄影（PET）或超声图像。 在至少一个放射图像中确定与一个或多个活检样本中的每一个对应的位置。 使用集成显示器来显示对应于每个活检样本，放射图像和对应于放射图像中的每个活检样本的位置的组织学图像。 通过组合图像提取的特征和从放射图像中相应位置提取的特征，进行癌症分级，预后预测和治疗选择，整合病理信息和放射学信息。
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5.发明申请Systems and Method for Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized Via Boosted Classifiers 有权使用随机步行器分割的MR图像中自动前列腺定位的系统和方法通过增强分类器初始化公开(公告)号：US20130064439A1
公开(公告)日：2013-03-14
申请号：US13291615
申请日：2011-11-08
申请人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
发明人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
CPC分类号： G06K9/6226 , G06T7/11 , G06T7/143 , G06T7/194 , G06T2207/10088 , G06T2207/20081 , G06T2207/20156 , G06T2207/30081
摘要： Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
摘要翻译： T2加权MR图像中的自动前列腺定位有利于劳动密集型癌症成像技术。 提供了准确分割MR图像前列腺的方法和系统，并解决了前列腺解剖和疾病，强度不均匀性以及直肠内线圈引起的假象的巨大变化。 用基于强度的多级高斯混合模型预期最大化（GMM-EM）原始MR图像分段训练的增强分类器自动检测前列腺的中心。 形状模型与多标签随机Walker（MLRW）结合使用，以限制MLRW内的播种过程。
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公开(公告)号：US09918700B2
公开(公告)日：2018-03-20
申请号：US13928763
申请日：2013-06-27
CPC分类号： A61B8/54 , A61B8/5223 , A61B8/585 , G06F19/00 , G06F19/321 , G16H30/20 , G16H40/63 , G16H50/20
摘要： Values for ultrasound acquisition parameters are altered in a manifold space. The number of parameters to be set is reduced using a manifold. Virtual parameters different than the acquisition parameters are used to alter the greater number of acquisition parameters. In a further use, optimum image settings may be obtained in an automated system by measuring image quality for feeding back to virtual parameter adjustment.
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公开(公告)号：US09594141B2
公开(公告)日：2017-03-14
申请号：US13633908
申请日：2012-10-03
申请人： Daniel Weller , Leo Grady , Lawrence Wald , Vivek K Goyal
发明人： Daniel Weller , Leo Grady , Lawrence Wald , Vivek K Goyal
IPC分类号： G01R33/341 , G01R33/58 , G01R33/561
CPC分类号： G01R33/5611
摘要： An MR imaging system uses multiple RF coils for acquiring corresponding multiple image data sets of a slice or volume of patient anatomy. An image data processor comprises at least one processing device conditioned for, deriving a first set of weights for weighted combination of k-space data of the multiple image data sets for generating a calibration data set comprising a subset of k-space data of composite image data representing the multiple image data sets. The image data processor uses the calibration data set in generating a first MR image data set, deriving the parameters of a probability distribution in response to the first set of weights and the first MR image data set and deriving a second set of weights and second MR image data set together using the probability distribution.
摘要翻译： MR成像系统使用多个RF线圈来获取患者解剖结构的切片或体积的相应的多个图像数据集。 图像数据处理器包括至少一个处理装置，该处理装置被调节为：为多个图像数据集的k空间数据的加权组合推导第一组加权，以产生包括合成图像的k空间数据子集的校准数据集 表示多个图像数据集的数据。 图像数据处理器使用校准数据集来生成第一MR图像数据集，响应于第一组权重和第一MR图像数据集导出概率分布的参数并导出第二组权重和第二MR 使用概率分布将图像数据集合在一起。
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公开(公告)号：US08823374B2
公开(公告)日：2014-09-02
申请号：US13326622
申请日：2011-12-15
IPC分类号： G01V3/00 , G01R33/561
CPC分类号： G01R33/5611
摘要： An MR imaging system uses the multiple RF coils for acquiring corresponding multiple image data sets of the slice. An image data processor comprises at least one processing device conditioned for, generating a composite MR image data set representing a single image in a single non-iterative operation by performing a weighted combination of luminance representative data of individual corresponding pixels of the multiple image data sets in providing an individual pixel luminance value of the composite MR image data set. The image data processor reduces noise in the composite MR image data set by generating a reduced set of significant components in a predetermined transform domain representation of data representing the composite image to provide a de-noised composite MR image data set. An image generator comprises at least one processing device conditioned for, generating a composite MR image using the de-noised composite MR image data set.
摘要翻译： MR成像系统使用多个RF线圈来获取切片的相应多个图像数据集。 图像数据处理器包括至少一个处理装置，该处理装置被调节为：通过执行多个图像数据集合中各个对应像素的亮度代表数据的加权组合来生成在单个非迭代操作中表示单个图像的复合MR图像数据集 提供复合MR图像数据集合的单独像素亮度值。 图像数据处理器通过在表示合成图像的数据的预定变换域表示中生成缩减的重要分量集合来降低复合MR图像数据集中的噪声，以提供去噪复合MR图像数据集。 图像生成器包括至少一个处理装置，该处理装置被调节为使用去噪合成MR图像数据集生成复合MR图像。
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公开(公告)号：US20130207652A1
公开(公告)日：2013-08-15
申请号：US13633908
申请日：2012-10-03
申请人： Daniel Weller , Leo Grady , Lawrence Wald , Vivek K. Goyal
发明人： Daniel Weller , Leo Grady , Lawrence Wald , Vivek K. Goyal
IPC分类号： G01R33/341 , G01R33/58
CPC分类号： G01R33/5611
摘要： An MR imaging system uses multiple RF coils for acquiring corresponding multiple image data sets of a slice or volume of patient anatomy. An image data processor comprises at least one processing device conditioned for, deriving a first set of weights for weighted combination of k-space data of the multiple image data sets for generating a calibration data set comprising a subset of k-space data of composite image data representing the multiple image data sets. The image data processor uses the calibration data set in generating a first MR image data set, deriving the parameters of a probability distribution in response to the first set of weights and the first MR image data set and deriving a second set of weights and second MR image data set together using the probability distribution.
摘要翻译： MR成像系统使用多个RF线圈来获取患者解剖结构的切片或体积的相应的多个图像数据集。 图像数据处理器包括至少一个处理装置，该处理装置被调节为：为多个图像数据集的k空间数据的加权组合推导第一组加权，以产生包括合成图像的k空间数据子集的校准数据集 表示多个图像数据集的数据。 图像数据处理器使用校准数据集来生成第一MR图像数据集，响应于第一组权重和第一MR图像数据集导出概率分布的参数并导出第二组权重和第二MR 使用概率分布将图像数据集合在一起。
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公开(公告)号：US20120057765A1
公开(公告)日：2012-03-08
申请号：US13182820
申请日：2011-07-14
申请人： Noha Youssry El-Zehiry , Leo Grady
发明人： Noha Youssry El-Zehiry , Leo Grady
CPC分类号： G06T5/002
摘要： A method for removing noise from an image includes receiving image data including a plurality of pixels. A graph including a plurality of nodes and a plurality of edges interconnecting the nodes is formulated. Each pixel of the image data is represented as a node of the graph and each edge of the graph is assigned a weight based on a penalty function applied to the nodes connected by the edge where the penalty function is less when a value of a given pixel of the plurality of pixels is between or equal to the values of two neighboring pixels than when the value of the given pixel is either greater than or less than the values of both of the two neighboring pixels. A total penalty of the graph is minimized. A denoised image is provided based on the total penalty-minimized graph.
摘要翻译： 从图像中去除噪声的方法包括接收包括多个像素的图像数据。 制定了包括多个节点和互连节点的多个边缘的图形。 图像数据的每个像素被表示为图形的节点，并且当给定像素的值时，基于施加到由边缘连接的节点的惩罚函数分配图的每个边缘的权重 与给定像素的值大于或小于两个相邻像素的值相比，多个像素的值在或等于两个相邻像素的值。 图形的总惩罚最小化。 基于总惩罚最小化图提供去噪图像。
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