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1.发明申请Semi-automatic Segmentation of Cardiac Ultrasound Images using a Dynamic Model of the Left Ventricle 审中-公开使用左心室动态模型对心脏超声图像进行半自动分割公开(公告)号：US20090161926A1
公开(公告)日：2009-06-25
申请号：US12028884
申请日：2008-02-11
CPC分类号： A61B8/00 , A61B8/0883 , G06T7/12 , G06T7/149 , G06T2207/10016 , G06T2207/10132 , G06T2207/20104 , G06T2207/30048
摘要： A method for segmenting a sequence of images includes developing an autoregressive model using training data including segmented images of a same type as the sequence of images. The sequence of images showing a progression of a subject through a cycle is acquired. At least two images from the sequence of images are identified. A region of interest is manually segmented from the identified images. The manually segmented images are parameterized. The autoregressive model is adapted to the parameterized segmented images. The autoregressive model is used to perform segmentation on the region of interest for a plurality of images of the sequence of images.
摘要翻译： 用于分割图像序列的方法包括使用包括与图像序列相同类型的分割图像的训练数据来开发自回归模型。 获取通过循环显示受检者进展的图像序列。 识别来自图像序列的至少两个图像。 感兴趣的区域是从识别的图像手动分割的。 手动分割的图像被参数化。 自回归模型适用于参数化分割图像。 自回归模型用于对图像序列的多个图像在感兴趣区域上进行分割。
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公开(公告)号：US08073252B2
公开(公告)日：2011-12-06
申请号：US11754476
申请日：2007-05-29
IPC分类号： G06K9/34
CPC分类号： G06T7/149 , G06T7/12 , G06T7/143 , G06T2207/10072 , G06T2207/20081 , G06T2207/30056
摘要： A computer readable medium is provided embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target. The method including learning prior knowledge, providing volume data comprising the target, selecting a plurality of key contours of the image of the target, building a 3D spare model of the image of the target given the plurality of key contours, segmenting the image of the target given the 3D sparse model, and outputting a segmentation of the image of the target.
摘要翻译： 提供了一种计算机可读介质，其包含可由处理器执行的指令，以执行用于目标的3D扫描的稀疏卷分割的方法。 该方法包括学习预先知识，提供包括目标的体数据，选择目标图像的多个关键轮廓，构建给定多个关键轮廓的目标图像的3D备用模型，分割图像的图像 给定3D稀疏模型的目标，并输出目标图像的分割。
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公开(公告)号：US20110123095A1
公开(公告)日：2011-05-26
申请号：US11754476
申请日：2007-05-29
IPC分类号： G06K9/00
CPC分类号： G06T7/149 , G06T7/12 , G06T7/143 , G06T2207/10072 , G06T2207/20081 , G06T2207/30056
摘要： A computer readable medium is provided embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target. The method including learning prior knowledge, providing volume data comprising the target, selecting a plurality of key contours of the image of the target, building a 3D spare model of the image of the target given the plurality of key contours, segmenting the image of the target given the 3D sparse model, and outputting a segmentation of the image of the target.
摘要翻译： 提供了一种计算机可读介质，其包含可由处理器执行的指令，以执行用于目标的3D扫描的稀疏卷分割的方法。 该方法包括学习预先知识，提供包括目标的体数据，选择目标图像的多个关键轮廓，构建给定多个关键轮廓的目标图像的3D备用模型，分割图像的图像 给定3D稀疏模型的目标，并输出目标图像的分割。
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公开(公告)号：US20060251325A1
公开(公告)日：2006-11-09
申请号：US11265586
申请日：2005-11-02
IPC分类号： G06K9/34
CPC分类号： G06T7/143 , G06T7/12 , G06T2207/10081 , G06T2207/20101 , G06T2207/30101
摘要： A system and method for particle filter based vessel segmentation are provided, the system including a processor, a Particle Filter unit in signal communication with the processor, and a Vessel Segmentation unit in signal communication with the processor; and the method including receiving image data for a vessel, initializing the vessel, modeling successive planes of the vessel as unknown states of a sequential process, and using a Particle Filter with a Monte Carlo sampling rule to propagate a plurality of segmentation hypotheses in parallel.
摘要翻译： 提供了一种用于基于粒子滤波器的血管分割的系统和方法，该系统包括处理器，与处理器进行信号通信的粒子滤波器单元和与处理器进行信号通信的血管分割单元; 并且所述方法包括接收血管的图像数据，初始化血管，将血管的连续平面建模为顺序过程的未知状态，以及使用具有蒙特卡洛采样规则的粒子滤波器并行地传播多个分割假设。
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5.发明申请Method for detection and tracking of deformable objects using adaptive time-varying autoregressive model 审中-公开使用自适应时变自回归模型检测和跟踪可变形物体的方法公开(公告)号：US20070098221A1
公开(公告)日：2007-05-03
申请号：US11511527
申请日：2006-08-28
CPC分类号： G06K9/32 , G06K9/00369 , G06T7/251 , G06T2207/10016 , G06T2207/30196
摘要： A method is provided for segmenting a moving object immersed in a background, comprising: obtaining a time-varying autoregressive model of prior motion of the object to predict future motion of the object; predicting a subsequent contour of the object from the background using the obtaining time-varying autoregressive model comprising using the obtained time-varying autoregressive model to initialize and/or constrain segmentation of the object from the background, and segmenting the object using the predicted subsequent contour and updating the autoregressive model while tracking of the segmented object.
摘要翻译： 提供了一种用于分割浸在背景中的运动物体的方法，包括：获得物体的先前运动的时变自回归模型，以预测物体的未来运动; 使用获得的时变自回归模型从背景预测对象的后续轮廓，包括使用所获得的时变自回归模型来初始化和/或约束来自背景的对象的分割，以及使用预测的后续轮廓来分割对象 并在跟踪分段对象的同时更新自回归模型。
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公开(公告)号：US20060251304A1
公开(公告)日：2006-11-09
申请号：US11374794
申请日：2006-03-14
IPC分类号： G06K9/00
CPC分类号： G06T7/143 , G06T7/12 , G06T2207/10072 , G06T2207/20101 , G06T2207/30101
摘要： A method of segmenting tubular structures in digital images comprises providing a digitized image, selecting a point within an object for segmenting in the image, defining an initial state of the selected point in the object, performing an initial segmentation of a 2D cross section of the object based on the initial state, predicting a new state of said object about a new point that is a translation of said selected point along the object tangent, correcting said new state prediction based on a measurement of said new point in said image, and segmenting a 2D cross section of said object based on said new state.
摘要翻译： 在数字图像中分割管状结构的方法包括提供数字化图像，选择对象内的点以在图像中分割，定义对象中所选点的初始状态，执行对象的2D截面的初始分割 基于初始状态来预测所述对象的新状态，关于作为所述选定点沿对象切线的平移的新点，基于所述图像中的所述新点的测量来校正所述新状态预测，以及分割 基于所述新状态的所述对象的2D横截面。
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公开(公告)号：US20060239541A1
公开(公告)日：2006-10-26
申请号：US11384894
申请日：2006-03-20
CPC分类号： G06K9/34 , G06K2009/00932 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20081 , G06T2207/20101 , G06T2207/30101
摘要： A method of segmenting tubular structures in digital images includes selecting a point in an image of a tubular object to be segmented, defining an initial state of the selected point, initializing measurement weights, a conditional probability distribution and a prior probability distribution of a feature space of the initial state, sampling the feature space from the prior probability distribution, estimating a posterior probability distribution by summing sample measurements weighted by the measurement weights, and segmenting a cross section of the tubular object from the posterior probability distribution.
摘要翻译： 在数字图像中分割管状结构的方法包括：选择要分割的管状物体的图像中的点，定义所选点的初始状态，初始化测量权重，条件概率分布和特征空间的先验概率分布 的初始状态，从先验概率分布中抽取特征空间，通过对由测量权重加权的样本测量求和来估计后验概率分布，以及从后验概率分布中分割管状对象的横截面。
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8.发明申请AUTOMATIC ORGAN DETECTION USING MACHINE LEARNING AND CLASSIFICATION ALGORITHMS 有权使用机器学习和分类算法的自动机器检测公开(公告)号：US20080154565A1
公开(公告)日：2008-06-26
申请号：US11747961
申请日：2007-05-14
申请人： Charles Florin , James Williams
发明人： Charles Florin , James Williams
IPC分类号： G06G7/60
CPC分类号： G06K9/6256 , G06K2209/05 , G06T7/70 , G06T2207/10072 , G06T2207/30092
摘要： A method and apparatus of visually depicting an organ, having the steps of choosing a predefined set features for analysis, the predefined set of features having distinguishing weak learners for an algorithm, wherein the predefined set of features and the weak learners chosen distinguish features of the organ desired to be represented, developing a strong classifier for the algorithm for the organ desired to be represented based upon the weak learners for the organ, one of conducing a body scan to produce a body scan data set and obtaining a body scan data set of information for a patient, applying the strong classifier and the algorithm to the body scan data set to develop a result of a representation of the organ and outputting the result of the step of applying of the strong classifier and the algorithm to the body scan data set to represent the organ.
摘要翻译： 一种视觉描绘器官的方法和装置，具有选择用于分析的预定义集合特征的步骤，所述预定义特征集合具有针对算法的区别弱学习者，其中所选择的特定组和弱学习者选择区分特征的特征 期望被表示的器官，为基于用于器官的弱学习者所希望表示的器官的算法开发强分类器，其中一个是进行身体扫描以产生身体扫描数据集并且获得身体扫描数据集 用于患者的信息，将强分类器和算法应用于身体扫描数据集以产生器官表示的结果，并将强分类器和算法的步骤的结果输出到身体扫描数据集 代表器官。
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9.发明申请Method and apparatus for generating a 2D image having pixels corresponding to voxels of a 3D image 有权用于生成具有与3D图像的体素对应的像素的2D图像的方法和装置公开(公告)号：US20060251307A1
公开(公告)日：2006-11-09
申请号：US11398210
申请日：2006-04-04
IPC分类号： G06K9/00
CPC分类号： G06T11/008 , G06T15/08
摘要： Disclosed is a method and apparatus for generating a two dimensional (2D) image of a structure (e.g., an organ) that has at least one pixel corresponding to at least one voxel of a three dimensional (3D) image of the structure. First, the surface of the structure in the 3D image is modeled by a geometrical volume such as an ellipsoid. Next, normal maximum intensity projection (MIP) rays are cast (i.e., projected) for voxels of the geometrical volume. The 2D image is then generated using the rays. The 2D image has at least one pixel that corresponds to at least one voxel of the 3D image.
摘要翻译： 公开了一种用于生成具有与该结构的三维（3D）图像的至少一个体素相对应的至少一个像素的结构（例如，器官）的二维（2D）图像的方法和装置。 首先，3D图像中的结构的表面由诸如椭圆体的几何体积建模。 接下来，对于几何体积的体素，投射（即投影）正常的最大强度投影（MIP）射线。 然后使用光线生成2D图像。 2D图像具有对应于3D图像的至少一个体素的至少一个像素。
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10.发明授权公开(公告)号：US07894653B2
公开(公告)日：2011-02-22
申请号：US11747961
申请日：2007-05-14
申请人： Charles Florin , James Williams
发明人： Charles Florin , James Williams
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K2209/05 , G06T7/70 , G06T2207/10072 , G06T2207/30092
摘要： A method and apparatus of visually depicting an organ, having the steps of choosing a predefined set features for analysis, the predefined set of features having distinguishing weak learners for an algorithm, wherein the predefined set of features and the weak learners chosen distinguish features of the organ desired to be represented, developing a strong classifier for the algorithm for the organ desired to be represented based upon the weak learners for the organ, one of conducing a body scan to produce a body scan data set and obtaining a body scan data set of information for a patient, applying the strong classifier and the algorithm to the body scan data set to develop a result of a representation of the organ and outputting the result of the step of applying of the strong classifier and the algorithm to the body scan data set to represent the organ.
摘要翻译： 一种视觉描绘器官的方法和装置，具有选择用于分析的预定义集合特征的步骤，所述预定义特征集合具有针对算法的区别弱学习者，其中所选择的特定组和弱学习者选择区分特征的特征 期望被表示的器官，为基于用于器官的弱学习者所希望表示的器官的算法开发强分类器，其中一个是进行身体扫描以产生身体扫描数据集并且获得身体扫描数据集 用于患者的信息，将强分类器和算法应用于身体扫描数据集以产生器官表示的结果，并将强分类器和算法的步骤的结果输出到身体扫描数据集 代表器官。
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