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1. 发明申请

US20060087703A1 Mutual information regularized Bayesian framework for multiple image restoration 失效
标题翻译：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。

2. 发明申请

US20060078181A1 Image compounding based on independent noise constraint 失效
公开(公告)号：US20060078181A1
公开(公告)日：2006-04-13
申请号：US11229106
申请日：2005-09-16
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： G06K9/00
CPC分类号： G06T5/002 , G06T5/50 , G06T2207/10081 , G06T2207/10132 , G06T2207/20076 , G06T2207/20192 , G06T2207/30004
摘要： A method and system for improving image quality by compounding a plurality of images to mitigate the effects of image noise. The method utilizes the independency between noise components for multiple image compounding. An effective measurement is designed to regularize the independency between noise in a traditional generative model based filtering framework, thereby enabling a more robust algorithmic solution to inaccurate signal/noise modeling. The method generally comprises selecting a plurality of images, calculating the residual error on each image; calculating the noise likelihood of each image, calculating the signal likelihood of the image, performing an independence analysis to regularize an independence constraint between the residual errors of the images, and summing the signal likelihood, noise likelihood and pairwise independency to approximate the joint independency between the residual errors.

3. 发明申请

US20070189607A1 SYSTEM AND METHOD FOR EFFICIENT FEATURE DIMENSIONALITY AND ORIENTATION ESTIMATION 审中-公开
标题翻译：用于有效特征尺度和方位估计的系统和方法
公开(公告)号：US20070189607A1
公开(公告)日：2007-08-16
申请号：US11548714
申请日：2006-10-12
申请人： Yunqiang Chen , Tong Fang , Jason Tyan
发明人： Yunqiang Chen , Tong Fang , Jason Tyan
IPC分类号： G06K9/48
CPC分类号： G06K9/46 , G06T7/73
摘要： A method of automatically detecting features in an image includes: designing a gradient detection filter and a line detection filter; applying the gradient detection filter and line detection filter to detect structures in an image; and estimating feature dimensionality and orientation of the detected structures in the image. The computation cost of gradient detection and line detection when applied on an image is a constant number of operations independent of the size of the gradient and line detection filters.
摘要翻译：一种自动检测图像中的特征的方法包括：设计梯度检测滤波器和线路检测滤波器; 应用梯度检测滤波器和线检测滤波器来检测图像中的结构; 并且估计图像中检测到的结构的特征维度和取向。应用于图像时梯度检测和线检测的计算成本是与梯度和线检测滤波器的大小无关的常数运算。

4. 发明申请

US20070217566A1 System and Method For Image Reconstruction 有权
标题翻译：图像重构系统与方法
公开(公告)号：US20070217566A1
公开(公告)日：2007-09-20
申请号：US11682013
申请日：2007-03-05
申请人： Yunqiang Chen , Lin Cheng , Tong Fang , Jason Tyan
发明人： Yunqiang Chen , Lin Cheng , Tong Fang , Jason Tyan
IPC分类号： H05G1/60 , A61B6/00 , G01N23/00 , G21K1/12
CPC分类号： G01N23/046 , G01N2223/419 , G06T11/006 , G06T2211/421 , G06T2211/424
摘要： A system and method for image reconstruction is disclosed. The method divides iterative image reconstruction into two stages, in the image and Radon space, respectively. In the first stage, filtered back projection and adaptive filtering in the image space are combined to generate a refined reconstructed image of a sinogram residue. This reconstructed image represents an update direction in the image space. In the second stage, the update direction is transformed to the Radon space, and a step size is determined to minimize a difference between the sinogram residue and a Radon transform of the refined reconstructed image of the sinogram residue in the Radon space. These stages are repeated iteratively until the solution converges.
摘要翻译：公开了一种用于图像重建的系统和方法。该方法分别在图像和氡空间中将迭代图像重建分为两个阶段。在第一阶段，滤波反投影和图像空间中的自适应滤波被组合以产生正弦图残差的精细重建图像。该重建图像表示图像空间中的更新方向。在第二阶段中，将更新方向转换为Radon空间，并且确定步长以最小化Radon空间中的正弦图残差的精细重建图像的正弦图残差和Radon变换之间的差异。迭代重复这些阶段，直到解得到收敛。

5. 发明申请

US20070147682A1 System and Method For Feature Detection In Image Sequences 审中-公开
标题翻译：图像序列中特征检测的系统和方法
公开(公告)号：US20070147682A1
公开(公告)日：2007-06-28
申请号：US11566353
申请日：2006-12-04
申请人： Ti-chiun Chang , Tong Fang , Jason Tyan , Yunqiang Chen
发明人： Ti-chiun Chang , Tong Fang , Jason Tyan , Yunqiang Chen
IPC分类号： G06K9/46
CPC分类号： G06K9/52 , G06T7/246 , G06T2207/30004
摘要： A method for processing image data includes inputting image data, determining a plurality of quadrature filter pairs based on filter parameter values to detect features of interest in the image data, applying the quadrature filter pairs to the image data to obtain a set of filter responses, and processing the filter responses to obtain the features of interest in the image data.
摘要翻译：一种处理图像数据的方法包括输入图像数据，基于滤波器参数值确定多个正交滤波器对，以检测图像数据中感兴趣的特征，将正交滤波器对应用于图像数据以获得一组滤波器响应，并处理滤波器响应以获得图像数据中感兴趣的特征。

6. 发明申请

US20070140582A1 Systems and Methods For Reducing Noise In Image Sequences 审中-公开
标题翻译：降低图像序列噪声的系统和方法
公开(公告)号：US20070140582A1
公开(公告)日：2007-06-21
申请号：US11539212
申请日：2006-10-06
申请人： Yunqiang Chen , Chunxiao Zhou , Tong Fang , Jason Tyan
发明人： Yunqiang Chen , Chunxiao Zhou , Tong Fang , Jason Tyan
IPC分类号： G06K9/40
CPC分类号： G06T5/002 , G06T5/20 , G06T5/50 , G06T7/20 , G06T2207/10016 , G06T2207/10116 , G06T2207/20012 , G06T2207/20182 , G06T2207/20192 , G06T2207/30004
摘要： A method for reducing noise in an image sequence includes: detecting structures in an image sequence; estimating lighting changes in the image sequence and compensating for the estimated lighting changes; detecting moving structures in the image sequence using the detected structures after compensating for the estimated lighting changes in the image sequence; and adaptively filtering imaging noise in the image sequence.
摘要翻译：一种用于降低图像序列中的噪声的方法包括：检测图像序列中的结构; 估计图像序列中的照明变化并补偿估计的照明变化; 在补偿图像序列中的估计的照明变化之后，使用检测到的结构来检测图像序列中的移动结构; 并自适应地过滤图像序列中的成像噪声。

7. 发明申请

US20060072844A1 Gradient-based image restoration and enhancement 失效
公开(公告)号：US20060072844A1
公开(公告)日：2006-04-06
申请号：US11231435
申请日：2005-09-20
申请人： Hongcheng Wang , Yunqiang Chen , Tong Fang , Jason Tyan
发明人： Hongcheng Wang , Yunqiang Chen , Tong Fang , Jason Tyan
IPC分类号： G06K9/40
CPC分类号： G06K9/40 , G06T5/002 , G06T5/003 , G06T5/20 , G06T2207/20192 , G06T2207/30004
摘要： A gradient-based image enhancement and restoration method and system which applies an orientation-isotropy adaptive filter to the gradients of high structured regions, and directly suppresses the gradients in the noise or texture regions. A new gradient field is obtained from which image reconstruction can progress using least mean squares. The method generally comprises: inputting image data; calculating image gradients; defining the gradients as having large or small coherence; filtering the large coherence gradients for edge enhancement; suppressing the small coherence gradients for noise reduction; assembling an enhanced gradient field from the filtered large coherence and suppressed small coherence gradients; and optimizing the assembled gradient field into a restored image.

8. 发明申请

US20070232914A1 System and Method For Adaptive Spatial Compounding For Ultrasound Imaging 有权
标题翻译：用于超声成像的自适应空间复合的系统和方法
公开(公告)号：US20070232914A1
公开(公告)日：2007-10-04
申请号：US11566328
申请日：2006-12-04
申请人： Yunqiang Chen , Jason Tyan
发明人： Yunqiang Chen , Jason Tyan
IPC分类号： A61B8/00
CPC分类号： A61B8/00 , A61B8/5238
摘要： A method for removing speckle noise from ultrasound images includes providing a plurality of digitized ultrasound (US) images, each image comprising a plurality of intensities corresponding to a domain of points on a 2-dimensional grid, initializing an initial gain associated with each of said plurality of US images, estimating a signal sub-space by averaging over each US image divided by its associated gain, and estimating an updated gain by projecting its associated image into said signal sub-space. If an absolute difference of said updated gain and said initial gain is less than a pre-determined quantity, obtaining an averaged image from said signal sub-space, estimating an optimal Wiener filter from said plurality of US images and said averaged image, and filtering said averaged image with said Wiener filter, wherein said speckle noise is substantially minimized.
摘要翻译：一种用于从超声图像中去除斑点噪声的方法包括提供多个数字化超声波（US）图像，每个图像包括对应于二维网格上的点的域的多个强度，初始化与每个所述多个美国图像，通过对每个US图像进行平均而除以其相关联的增益来估计信号子空间，以及通过将其相关图像投影到所述信号子空间来估计更新的增益。如果所述更新的增益和所述初始增益的绝对差小于预定量，则从所述信号子空间获得平均图像，从所述多个US图像和所述平均图像中估计最佳维纳滤波器，以及滤波所述维纳滤波器的所述平均图像，其中所述斑点噪声基本上最小化。

9. 发明申请

US20060173874A1 Method and system for interactive parameter optimization using multi-dimensional scaling 审中-公开
标题翻译：使用多维缩放进行交互参数优化的方法和系统
公开(公告)号：US20060173874A1
公开(公告)日：2006-08-03
申请号：US11342150
申请日：2006-01-27
申请人： Yunqiang Chen , Jason Tyan
发明人： Yunqiang Chen , Jason Tyan
IPC分类号： G06F17/30
CPC分类号： G06K9/6251 , G06F17/11 , G06T5/20
摘要： A method for interactively optimizing a system comprises interactively adjusting controlling parameters in a parameter set, by deriving successive pluralities of parameter sets in a parameter space, each of whose respective member parameter sets are respectively ranked in order, utilizing a ranking input based on respective system performance associated with each parameter set and from each of which plurality of parameter sets an optimal parameter set is selected and used as a point of departure for deriving the next following plurality of parameter sets in the parameter space, in accordance with the preceding ranking, in conjunction with a step size and a step direction derived from the ranking, in accordance with multidimensional scaling (MDS).
摘要翻译：一种用于交互地优化系统的方法包括通过在参数空间中导出连续的多个参数集合来交互地调整参数集中的控制参数，每个参数集合各自的成员参数集合分别按顺序排列，利用基于相应系统的排名输入选择与每个参数集相关联的性能，并且从多个参数集合中选择最佳参数集合中的每个参数集合的性能，并将其用作根据先前排名导出参数空间中的下一个后续多个参数集的出发点，与根据多维缩放（MDS）从排名导出的步长和步长方向相结合。

10. 发明授权

US08355555B2 System and method for multi-image based virtual non-contrast image enhancement for dual source CT 有权
标题翻译：用于双源CT的基于多图像的虚拟非对比度图像增强的系统和方法
公开(公告)号：US08355555B2
公开(公告)日：2013-01-15
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： G06K9/00
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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