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    Mutual information regularized Bayesian framework for multiple image restoration
    2.
    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。

    Image compounding based on independent noise constraint
    3.
    发明申请
    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.

    System and Method For Image Reconstruction
    4.
    发明申请
    System and Method For Image Reconstruction 有权
    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。

    System and Method For Feature Detection In Image Sequences
    5.
    发明申请
    System and Method For Feature Detection In Image Sequences 审中-公开
    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。

    Gradient-based image restoration and enhancement
    6.
    发明申请
    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.

    SYSTEM AND METHOD FOR EFFICIENT FEATURE DIMENSIONALITY AND ORIENTATION ESTIMATION
    7.
    发明申请
    SYSTEM AND METHOD FOR EFFICIENT FEATURE DIMENSIONALITY AND ORIENTATION ESTIMATION 审中-公开
    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。

    System and method for multi-image based virtual non-contrast image enhancement for dual source CT
    8.
    发明授权
    System and method for multi-image based virtual non-contrast image enhancement for dual source CT 有权
    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。

    Method and system for correcting butting artifacts in X-ray images
    10.
    发明授权
    Method and system for correcting butting artifacts in X-ray images 有权
    用于校正X射线图像中的伪像的方法和系统

    公开(公告)号：US08073191B2

    公开(公告)日：2011-12-06

    申请号：US12283357

    申请日：2008-09-11

    申请人： Ti-chiun Chang ,  Yunqiang Chen ,  Tong Fang ,  Sandra Martin ,  Stefan Böhm ,  Peter Durlak

    发明人： Ti-chiun Chang ,  Yunqiang Chen ,  Tong Fang ,  Sandra Martin ,  Stefan Böhm ,  Peter Durlak

    IPC分类号： G06K9/00

    CPC分类号： G06K9/346 ,  G06K2209/05 ,  G06T5/005 ,  G06T5/20 ,  G06T2207/10116 ,  G06T2207/20012 ,  G06T2207/20076 ,  G06T2207/30004

    摘要： A method and system for correcting butting artifacts in x-ray images is disclosed. In order to correct a butting artifact in an x-ray image, a butting artifact region in the x-ray image is normalized. Multiple intensity shift estimators are calculated for each pixel of each line of the butting artifact. Confidence intervals are calculated for each intensity shift estimator. A multiple hypothesis hidden Markov model (MH-HMM) is formulated based on the intensity shift operators and confidence measures subject to a smoothness constraint, and the MH-HMM is solved to determine intensity shift values for each pixel. A corrected image is generated by adjusting the intensity of each pixel of the butting artifact based on the intensity shift value for that pixel.

    摘要翻译： 公开了一种用于校正X射线图像中的伪像的方法和系统。 为了校正X射线图像中的对接伪影，对x射线图像中的对接伪影区域进行归一化。 针对对接工件的每一行的每个像素计算多个强度偏移估计量。 为每个强度偏移估计器计算置信区间。 基于强度偏移算子和基于平滑度约束的置信度度度，制定了多重假设隐马尔可夫模型（MH-HMM），并求解了MH-HMM，以确定每个像素的强度偏移值。 通过基于该像素的强度偏移值调整对接伪影的每个像素的强度来生成校正图像。