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公开(公告)号：US07961925B2
公开(公告)日：2011-06-14
申请号：US11934273
申请日：2007-11-02
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
IPC分类号： G06K9/00
CPC分类号： G06K9/32 , G06K9/6857
摘要： A method and system for dual energy image registration is disclosed. In order to segment first and second images of a dual energy image pair, the first and second images are preprocessed to detect edges in the images. Gaussian pyramids, having multiple pyramid images corresponding to multiple pyramid levels, are generated for the first and second images. An initial optical flow value is initialized for a first pyramid level, and the optical flow value is sequentially updated for each pyramid level based on the corresponding pyramid images using an optimization function having a similarity measure and a regularizer. This results in a final optical flow value between the first and second images, and the first and second images are registered based on the final optical flow value.
摘要翻译： 公开了一种用于双能量图像配准的方法和系统。 为了分割双能量图像对的第一和第二图像，第一和第二图像被预处理以检测图像中的边缘。 对于第一和第二图像，生成具有对应于多个金字塔级别的多个金字塔图像的高斯金字塔。 对于第一金字塔级别初始化初始光流值，并且使用具有相似性度量和正则化器的优化函数，基于相应的金字塔图像，针对每个金字塔级别顺序更新光流值。 这导致第一和第二图像之间的最终光流值，并且基于最终光流值登记第一和第二图像。
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22.发明授权Mutual information regularized Bayesian framework for multiple image restoration 失效相互信息正则化贝叶斯框架用于多个图像恢复公开(公告)号：US07684643B2
公开(公告)日：2010-03-23
申请号：US11252334
申请日：2005-10-17
申请人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Jenn-Kwei Tyan
发明人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Jenn-Kwei Tyan
CPC分类号： G06T5/002 , G06T5/20 , G06T5/50 , G06T2207/10132 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004
摘要： A method for multiple image restoration includes receiving a plurality of images corrupted by noise, and initializing a reduced noise estimate of the plurality of images. The method further includes estimating a probability of distributions of noise around each pixel and the probability of the signal, estimating mutual information between noise on the plurality of images based on the probabilities of distributions of noise around each pixel and the joint distribution of noise, and updating each pixel within a search range to determine a restored image by reducing the mutual information between the noise on the plurality of images.
摘要翻译： 一种用于多重图像恢复的方法包括：接收由噪声破坏的多个图像，以及初始化所述多个图像的降低的噪声估计。 该方法还包括估计每个像素周围的噪声分布概率和信号的概率，基于每个像素周围的噪声分布的概率和噪声的联合分布来估计多个图像上的噪声之间的相互信息;以及 更新搜索范围内的每个像素以通过减少多个图像上的噪声之间的相互信息来确定恢复的图像。
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公开(公告)号：US20080112649A1
公开(公告)日：2008-05-15
申请号：US11934273
申请日：2007-11-02
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
IPC分类号： G06K9/60
CPC分类号： G06K9/32 , G06K9/6857
摘要： A method and system for dual energy image registration is disclosed. In order to segment first and second images of a dual energy image pair, the first and second images are preprocessed to detect edges in the images. Gaussian pyramids, having multiple pyramid images corresponding to multiple pyramid levels, are generated for the first and second images. An initial optical flow value is initialized for a first pyramid level, and the optical flow value is sequentially updated for each pyramid level based on the corresponding pyramid images using an optimization function having a similarity measure and a regularizer. This results in a final optical flow value between the first and second images, and the first and second images are registered based on the final optical flow value.
摘要翻译： 公开了一种用于双能量图像配准的方法和系统。 为了分割双能量图像对的第一和第二图像，第一和第二图像被预处理以检测图像中的边缘。 对于第一和第二图像，生成具有对应于多个金字塔级别的多个金字塔图像的高斯金字塔。 对于第一金字塔级别初始化初始光流值，并且使用具有相似性度量和正则化器的优化函数，基于相应的金字塔图像，针对每个金字塔级别顺序更新光流值。 这导致第一和第二图像之间的最终光流值，并且基于最终光流值登记第一和第二图像。
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公开(公告)号：US20070206880A1
公开(公告)日：2007-09-06
申请号：US11563815
申请日：2006-11-28
申请人： Yunqiang Chen , Hao Wu , Tong Fang
发明人： Yunqiang Chen , Hao Wu , Tong Fang
CPC分类号： G06K9/00 , G06K9/6206 , G06K2209/05 , G06T7/344
摘要： A computer implemented method for joint image registration and reconstruction of a plurality of images includes providing the plurality of images, modeling the plurality of images, including reconstructing bone and soft-tissue in respective images of the plurality of images, performing a hierarchical free-form registration of models of the plurality of images to determine a jointly registered and reconstructed image with successive accuracy adjustment according to a registration error, and outputting the registered and reconstructed image.
摘要翻译： 用于联合图像配准和重建多个图像的计算机实现方法包括提供多个图像，对多个图像进行建模，包括在多个图像的各个图像中重建骨骼和软组织，执行分层自由形式 登记多个图像的模型，以根据注册错误来确定具有连续精度调整的联合登记和重建图像，并输出注册和重建的图像。
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25.发明申请Mutual information regularized Bayesian framework for multiple image restoration 失效相互信息正则化贝叶斯框架用于多个图像恢复公开(公告)号：US20060087703A1
公开(公告)日：2006-04-27
申请号：US11252334
申请日：2005-10-17
申请人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Tyan
发明人： Yunqiang Chen , Hongcheng Wang , Tong Fang , Jason Tyan
IPC分类号： H04N1/38
CPC分类号： G06T5/002 , G06T5/20 , G06T5/50 , G06T2207/10132 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004
摘要： A method for multiple image restoration includes receiving a plurality of images corrupted by noise, and initializing a reduced noise estimate of the plurality of images. The method further includes estimating a probability of distributions of noise around each pixel and the probability of the signal, estimating mutual information between noise on the plurality of images based on the probabilities of distributions of noise around each pixel and the joint distribution of noise, and updating each pixel within a search range to determine a restored image by reducing the mutual information between the noise on the plurality of images.
摘要翻译： 一种用于多重图像恢复的方法包括：接收由噪声破坏的多个图像，以及初始化所述多个图像的降低的噪声估计。 该方法还包括估计每个像素周围的噪声分布概率和信号的概率，基于每个像素周围的噪声分布的概率和噪声的联合分布来估计多个图像上的噪声之间的相互信息;以及 更新搜索范围内的每个像素以通过减少多个图像上的噪声之间的相互信息来确定恢复的图像。
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公开(公告)号：US08005288B2
公开(公告)日：2011-08-23
申请号：US12101487
申请日：2008-04-11
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
CPC分类号： G06T5/50 , G06T5/005 , G06T7/33 , G06T2207/10116 , G06T2207/10144 , G06T2207/30004
摘要： A system and method for layer reconstruction from dual-energy image pairs are provided, the method including: receiving a pair of dual-energy images, one having a relatively high energy dose and the other having a relatively low energy dose; ascertaining that a first relatively motionless layer is substantially aligned between the high and low dose images; computing a preliminary image of a second layer that has non-rigid motion relative to the first layer; detecting the relative motion of the second layer relative to the first layer; generating a mask in accordance with the detected motion; filling the motion area corresponding to the mask with gradients of the high-dose image; removing the first layer; and inpainting the motion area.
摘要翻译： 提供了一种用于从双能量图像对进行层重构的系统和方法，所述方法包括：接收一对双能量图像，一个具有相对较高的能量剂量，另一个具有相对低的能量剂量; 确定第一相对静止层基本上在高剂量图像和低剂量图像之间对准; 计算相对于第一层具有非刚性运动的第二层的初步图像; 检测第二层相对于第一层的相对运动; 根据检测到的运动产生掩模; 用高剂量图像的梯度填充对应于掩模的运动区域; 去除第一层; 并修复运动区域。
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27.发明申请公开(公告)号：US20110064292A1
公开(公告)日：2011-03-17
申请号：US12854341
申请日：2010-08-11
申请人： Yunqiang Chen , Tong Fang , Bernhard Schmidt , Rainer Raupach
发明人： Yunqiang Chen , Tong Fang , Bernhard Schmidt , Rainer Raupach
IPC分类号： G06T5/00
CPC分类号： G06T5/002 , G06T5/50 , G06T2207/10072 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004 , G06T2207/30016
摘要： A method for enhancing a virtual non-contrast image, includes receiving a pair of dual scan CT images and calculating a virtual non-contrast image from the pair of CT images using known tissue attenuation coefficients. A conditional probability distribution is estimated for tissue at first and second points in each of the pair of CT images and the virtual non-contrast image as being the same type. A conditional probability distribution for tissue is estimated at the first and second points in each of the pair of CT images and the virtual non-contrast image as being of different types. An a posteriori probability of the tissue at the first and second points as being the same type is calculated from the conditional probability distributions, and an enhanced virtual non-contrast image is calculated using the a posteriori probability of the tissue at the first and second points as being the same type.
摘要翻译： 一种用于增强虚拟非对比度图像的方法，包括接收一对双扫描CT图像，并使用已知的组织衰减系数从所述一对CT图像计算虚拟非对比度图像。 对于一对CT图像和虚拟非对比度图像中的每一个中的第一和第二点处的组织估计为相同类型的条件概率分布。 在一对CT图像和虚拟非对比度图像中的每一个中的第一和第二点处估计组织的条件概率分布为不同类型。 根据条件概率分布计算第一和第二点处的组织作为相同类型的后验概率，并且使用在第一和第二点处的组织的后验概率来计算增强的虚拟非对比度图像 作为同一类型。
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公开(公告)号：US07783096B2
公开(公告)日：2010-08-24
申请号：US11675258
申请日：2007-02-15
申请人： Yunqiang Chen , Hao Wu , Tong Fang
发明人： Yunqiang Chen , Hao Wu , Tong Fang
IPC分类号： G06K9/00
CPC分类号： A61B6/00 , A61B6/482 , G01N2223/206 , G01N2223/423 , G06T7/207 , G06T2207/20016 , G06T2207/30008
摘要： Certain exemplary embodiments comprise a method, which can comprise determining an image of a predetermined physiological structure of a patient. The image can be determined based upon a first set of image data of the predetermined physiological structure of the patient. The image can be based upon a second set of image data of the predetermined physiological structure of the patient. The image can be determined based upon an iteratively adjusted movement of the patient.
摘要翻译： 某些示例性实施例包括可以包括确定患者的预定生理结构的图像的方法。 可以基于患者的预定生理结构的第一组图像数据来确定图像。 图像可以基于患者的预定生理结构的第二组图像数据。 可以基于患者的迭代调整的运动来确定图像。
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29.发明申请公开(公告)号：US20090116722A1
公开(公告)日：2009-05-07
申请号：US12287551
申请日：2008-10-10
申请人： Yunqiang Chen , Tong Fang
发明人： Yunqiang Chen , Tong Fang
CPC分类号： G06T11/008 , G06T5/005 , G06T2207/20016
摘要： A method and system for soft tissue image reconstruction for dual x-ray imaging is disclosed. A multigrid PDE solver is used for solving a Poisson equation for soft tissue image reconstruction based on a soft tissue gradient field extracted from dual energy x-ray images. The divergence of the soft tissue gradient field is downsampled to a coarsest resolution level, and a soft tissue image is generated based on the divergence of the soft tissue gradient field at the coarsest level. The soft tissue image is interpolated to a next finest resolution level, and refined by at least one coarse grid correction cycle at the current resolution level. The coarse grid correction cycle calculates a defect based on the current soft tissue image, downsamples the defect to the coarsest level, calculates a correction based on the defect at the coarsest level, and upsamples the correction to the current resolution level to refine the current soft tissue image. The interpolation and refinement of the soft tissue image is repeated until the soft tissue image is refined at the finest resolution level.
摘要翻译： 公开了一种用于双重X射线成像的软组织图像重建的方法和系统。 基于从双能量x射线图像提取的软组织梯度场，多重PDE求解器用于求解软组织图像重建的泊松方程。 软组织梯度场的发散度被下采样到最粗分辨率水平，并且基于最粗糙度级别的软组织梯度场的发散度产生软组织图像。 将软组织图像插值到下一个最佳分辨率级别，并通过至少一个粗网格校正周期以当前分辨率级别进行细化。 粗网格校正周期基于当前软组织图像计算缺陷，将缺陷下采样到最粗水平，基于最粗糙级别的缺陷计算校正，并将校正上采样到当前分辨率水平以细化当前软 组织图像 重复软组织图像的插值和细化，直到软组织图像以最好的分辨率水平被精炼。
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公开(公告)号：US20090087070A1
公开(公告)日：2009-04-02
申请号：US12283441
申请日：2008-09-12
申请人： Gregory G. Slabaugh , Tong Fang , Kooksang Moon , Yunqiang Chen
发明人： Gregory G. Slabaugh , Tong Fang , Kooksang Moon , Yunqiang Chen
IPC分类号： G06K9/00
CPC分类号： G06T5/002 , G06T5/005 , G06T5/20 , G06T2207/10116 , G06T2207/20064 , G06T2207/20076 , G06T2207/20081 , G06T2207/30008 , G06T2207/30064
摘要： A method and system for suppressing bone structures based on a single x-ray image is disclosed. The bone structure suppressing method predicts a soft-tissue image without bone structures from an input x-ray image. A set of features is extracted for each pixel of the input x-ray image. A soft-tissue image is then generated from the input x-ray image using a trained regression function to determine an intensity value for the soft-tissue image corresponding to each pixel of the input x-ray image based on the set of features extracted for each pixel of the input x-ray image. The extracted features can be wavelet features and the regression function can be trained using Bayesian Committee Machine (BCM) to approximate Gaussian process regression (GPR).
摘要翻译： 公开了一种基于单个X射线图像来抑制骨骼结构的方法和系统。 骨结构抑制方法从输入的X射线图像预测没有骨结构的软组织图像。 为输入x射线图像的每个像素提取一组特征。 然后使用训练回归函数从输入的X射线图像生成软组织图像，以基于为 输入x射线图像的每个像素。 提取的特征可以是小波特征，并且可以使用贝叶斯委员会机器（BCM）来训练回归函数来近似高斯过程回归（GPR）。
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