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公开(公告)号：US07680335B2
公开(公告)日：2010-03-16
申请号：US11371698
申请日：2006-03-09
CPC分类号： G06K9/6226 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20101 , G06T2207/30064
摘要： A system and method are provided for prior-constrained mean shift analysis of a data array, the system including a processor, an input adapter in signal communication with the processor for receiving at least one data array, and a prior constraints unit in signal communication with the processor for performing a prior-constrained mean shift analysis on the at least one data array; and the method including receiving initialization data, selecting an initial point relative to the initialization data, Gaussian fitting with a prior-constrained mean shift responsive to the initial point to parse a structure, and setting the parsed structure as a prior constraint.
摘要翻译： 提供了一种用于数据阵列的先前约束平均移位分析的系统和方法，该系统包括处理器，与处理器进行信号通信的输入适配器，用于接收至少一个数据阵列，以及与信号通信中的先前约束单元， 所述处理器用于对所述至少一个数据阵列执行先前约束的平均移位分析; 并且所述方法包括接收初始化数据，相对于初始化数据选择初始点，响应于初始点解析结构的先验约束平均移位的高斯拟合，以及将解析结构设置为先前约束。
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公开(公告)号：US07995809B2
公开(公告)日：2011-08-09
申请号：US11396854
申请日：2006-04-03
IPC分类号： G06K9/00
CPC分类号： G06T7/155 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/20008 , G06T2207/20116 , G06T2207/30064
摘要： By testing for nodule segmentation errors based on the scan data, juxtapleural cases are identified. Once identified, the scan data or subsequent estimation may be altered to account for adjacent rib, tissue, vessel or other structure effecting segmentation. One alteration is to shape a filter as a function of the scan data. For example, an originally estimated ellipsoid for the nodule segmentation defines the filter. The filter is used to identify the undesired information, and masking removes the undesired information for subsequent estimation of the nodule segmentation. Another possible alteration biases the subsequent estimation away from the incorrect information, such as the rib, tissue or vessel information influencing the original estimation. For example, a negative prior or probability is assigned to data corresponding to the originally estimated segmentation for the subsequent estimation.
摘要翻译： 通过基于扫描数据测试结节分割错误，确定并列案例。 一旦被识别，可以改变扫描数据或随后的估计，以考虑影响分割的相邻肋，组织，血管或其他结构。 一个改变是根据扫描数据来形成一个过滤器。 例如，用于结节分段的原始估计的椭圆体定义了滤波器。 滤波器用于识别不期望的信息，并且掩蔽去除了不期望的信息以用于随后估计结节分割。 另一种可能的改变偏离了不正确信息的后续估计，例如影响原始估计的肋骨，组织或血管信息。 例如，将负的先前或概率分配给对应于用于后续估计的最初估计的分割的数据。
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公开(公告)号：US20060269109A1
公开(公告)日：2006-11-30
申请号：US11396854
申请日：2006-04-03
CPC分类号： G06T7/155 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/20008 , G06T2207/20116 , G06T2207/30064
摘要： By testing for nodule segmentation errors based on the scan data, juxtapleural cases are identified. Once identified, the scan data or subsequent estimation may be altered to account for adjacent rib, tissue, vessel or other structure effecting segmentation. One alteration is to shape a filter as a function of the scan data. For example, an originally estimated ellipsoid for the nodule segmentation defines the filter. The filter is used to identify the undesired information, and masking removes the undesired information for subsequent estimation of the nodule segmentation. Another possible alteration biases the subsequent estimation away from the incorrect information, such as the rib, tissue or vessel information influencing the original estimation. For example, a negative prior or probability is assigned to data corresponding to the originally estimated segmentation for the subsequent estimation.
摘要翻译： 通过基于扫描数据测试结节分割错误，确定并列案例。 一旦被识别，可以改变扫描数据或随后的估计，以考虑影响分割的相邻肋，组织，血管或其他结构。 一个改变是根据扫描数据来形成一个过滤器。 例如，用于结节分段的原始估计的椭圆体定义了滤波器。 滤波器用于识别不期望的信息，并且掩蔽去除了不期望的信息以用于随后估计结节分割。 另一种可能的改变偏离了不正确信息的后续估计，例如影响原始估计的肋骨，组织或血管信息。 例如，将负的先前或概率分配给对应于用于后续估计的最初估计的分割的数据。
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公开(公告)号：US20060242218A1
公开(公告)日：2006-10-26
申请号：US11371698
申请日：2006-03-09
IPC分类号： G06F7/38
CPC分类号： G06K9/6226 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20101 , G06T2207/30064
摘要： A system and method are provided for prior-constrained mean shift analysis of a data array, the system including a processor, an input adapter in signal communication with the processor for receiving at least one data array, and a prior constraints unit in signal communication with the processor for performing a prior-constrained mean shift analysis on the at least one data array; and the method including receiving initialization data, selecting an initial point relative to the initialization data, Gaussian fitting with a prior-constrained mean shift responsive to the initial point to parse a structure, and setting the parsed structure as a prior constraint.
摘要翻译： 提供了一种用于数据阵列的先前约束平均移位分析的系统和方法，该系统包括处理器，与处理器进行信号通信的输入适配器，用于接收至少一个数据阵列，以及与信号通信中的先前约束单元， 所述处理器用于对所述至少一个数据阵列执行先前约束的平均移位分析; 并且所述方法包括接收初始化数据，相对于初始化数据选择初始点，响应于初始点解析结构的先验约束平均移位的高斯拟合，以及将解析结构设置为先前约束。
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5.发明授权Scale selection for anisotropic scale-space: application to volumetric tumor characterization 有权各向异性尺度空间的尺度选择：应用于体积肿瘤表征公开(公告)号：US07616792B2
公开(公告)日：2009-11-10
申请号：US10990888
申请日：2004-11-17
申请人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
发明人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
CPC分类号： G06K9/527 , G06T7/0012 , G06T7/11 , G06T2207/10081 , G06T2207/30064
摘要： A method for determining a structure in volumetric data includes determining an anisotropic scale-space for a local region around a given spatial local maximum, determining L-normalized scale-space derivatives in the anisotropic scale-space, and determining the presence of noise in the volumetric data and upon determining noise in the volumetric data, determining the structure by a most-stable-over-scales determination, and upon determining noise below a desirable level, determining the structure by one of the most-stable-over-scales determination and a maximum-over-scales determination.
摘要翻译： 一种用于确定体积数据中的结构的方法包括确定围绕给定空间局部最大值的局部区域的各向异性尺度空间，确定各向异性尺度空间中的L归一化尺度空间导数，以及确定在 体积数据和确定体积数据中的噪声，通过最稳定的超标度确定来确定结构，并且在将噪声确定在理想水平之下时，通过最稳定的超尺度确定之一来确定结构;以及 最大超尺度测定。
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6.发明申请System and method for volumetric tumor segmentation using joint space-intensity likelihood ratio test 有权使用联合空间 - 强度似然比检验的体积肿瘤分割的系统和方法公开(公告)号：US20060050958A1
公开(公告)日：2006-03-09
申请号：US11184590
申请日：2005-07-19
申请人： Kazunori Okada , Umut Akdemir , Arun Krishnan
发明人： Kazunori Okada , Umut Akdemir , Arun Krishnan
IPC分类号： G06K9/34
CPC分类号： G06T7/143 , G06T7/11 , G06T7/194 , G06T2207/10072 , G06T2207/30096
摘要： A method for segmenting a digitized image includes providing a digitized volumetric image comprising a plurality of intensities corresponding to a domain of points in an N-dimensional space, identifying a target structure in said image, forming a window about said target structure whose size is a function of the target scale, and performing a joint space-intensity-likelihood ratio test at each point within said window to determine whether each said point is within said target structure.
摘要翻译： 用于分割数字化图像的方法包括提供数字化的体积图像，其包括对应于N维空间中的点的域的多个强度，识别所述图像中的目标结构，形成关于所述目标结构的窗口，其大小为 功能，并且在所述窗口内的每个点处执行联合空间 - 强度 - 似然比测试，以确定每个所述点是否在所述目标结构内。
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7.发明申请Scale selection for anisotropic scale-space: application to volumetric tumor characterization 有权各向异性尺度空间的尺度选择：应用于体积肿瘤表征公开(公告)号：US20050135663A1
公开(公告)日：2005-06-23
申请号：US10990888
申请日：2004-11-17
申请人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
发明人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
CPC分类号： G06K9/527 , G06T7/0012 , G06T7/11 , G06T2207/10081 , G06T2207/30064
摘要： A method for determining a structure in volumetric data includes determining an anisotropic scale-space for a local region around a given spatial local maximum, determining L-normalized scale-space derivatives in the anisotropic scale-space, and determining the presence of noise in the volumetric data and upon determining noise in the volumetric data, determining the structure by a most-stable-over-scales determination, and upon determining noise below a desirable level, determining the structure by one of the most-stable-over-scales determination and a maximum-over-scales determination.
摘要翻译： 一种用于确定体积数据中的结构的方法包括确定围绕给定空间局部最大值的局部区域的各向异性尺度空间，确定各向异性尺度空间中的L归一化尺度空间导数，以及确定在 体积数据和确定体积数据中的噪声，通过最稳定的超标度确定来确定结构，并且在将噪声确定在理想水平之下时，通过最稳定的超尺度确定之一来确定结构;以及 最大超尺度测定。
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公开(公告)号：US20050201606A1
公开(公告)日：2005-09-15
申请号：US10991683
申请日：2004-11-18
申请人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
发明人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
CPC分类号： G06K9/34 , G06K2209/053
摘要： A method for three-dimensional segmentation of a target in multislice images of volumetric data includes determining a center and a spread of the target by a parametric fitting of the volumetric data, and determining a three-dimensional volume by non-parametric segmentation of the volumetric data iteratively refining the center and spread of the target in the volumetric data.
摘要翻译： 用于体积数据的多层图像中的目标的三维分割的方法包括通过体积数据的参数拟合来确定目标的中心和扩展，以及通过体积数据的非参数分割来确定三维体积 数据迭代地改进了体积数据中的目标的中心和扩展。
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9.发明申请Volumetric characterization using covariance estimation from scale-space hessian matrices 有权使用尺度空间非线性矩阵的协方差估计的体积特征公开(公告)号：US20050096525A1
公开(公告)日：2005-05-05
申请号：US10954741
申请日：2004-09-30
申请人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
发明人： Kazunori Okada , Dorin Comaniciu , Arun Krishnan
CPC分类号： G06T7/11 , G06T2207/10081 , G06T2207/30064
摘要： A method for determining a volume of interest in data includes determining fixed-bandwidth estimations of a plurality of analysis bandwidths, wherein the estimation of the fixed-bandwidth comprises, providing an estimate of a mode location of the volume of interest in the data, and determining a covariance of the volume of interest using a local Hessian matrix. The method further includes determining the volume of interest as a most stable fixed-bandwidth estimation across each of the plurality of analysis bandwidths.
摘要翻译： 一种用于确定数据中的感兴趣体积的方法包括：确定多个分析带宽的固定带宽估计，其中固定带宽的估计包括：提供数据中感兴趣体积的模式位置的估计，以及 使用局部Hessian矩阵确定感兴趣体积的协方差。 该方法还包括将感兴趣的体积确定为跨越多个分析带宽中的每一个的最稳定的固定带宽估计。
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10.发明授权Robust click-point linking with geometric configuration context: interactive localized registration approach 有权强大的点击链接与几何配置上下文：交互式本地化注册方法公开(公告)号：US07903857B2
公开(公告)日：2011-03-08
申请号：US11705375
申请日：2007-02-12
申请人： Xiaolei Huang , Arun Krishnan , Kazunori Okada , Xiang Zhou
发明人： Xiaolei Huang , Arun Krishnan , Kazunori Okada , Xiang Zhou
CPC分类号： G06K9/4671 , G06K9/6211
摘要： Disclosed is robust click-point linking, defined as estimating a single point-wise correspondence between data domains given a user-specified point in one domain or as an interactive localized registration of a monomodal data pair. To link visually dissimilar local regions, Geometric Configuration Context (GCC) is introduced. GCC represents the spatial likelihood of the point corresponding to the click-point in the other domain. A set of scale-invariant saliency features are pre-computed for both data. GCC is modeled by a Gaussian mixture whose component mean and width are determined as a function of the neighboring saliency features and their correspondences. This allows correspondence of dissimilar parts using only geometrical relations without comparing the local appearances. GCC models are derived for three transformation classes: pure translation, scaling and translation, and similarity transformation. For solving the linking problem, a variable-bandwidth mean shift method is adapted for estimating the maximum likelihood solution of the GCC.
摘要翻译： 公开了强大的点对点链接，定义为估计给定一个域中的用户指定点的数据域之间的单个点对应关系，或者作为单模数据对的交互式局部化注册。 为了链接视觉上不同的局部区域，引入了几何配置上下文（GCC）。 GCC表示对应于另一个域中的点击点的点的空间似然性。 对两个数据都预先计算了一组尺度不变的显着特征。 GCC由高斯混合物建模，其分量平均值和宽度被确定为邻近显着特征及其对应关系的函数。 这允许不相似部分的对应使用仅几何关系而不比较局部外观。 GCC模型是为三个转换类派生的：纯翻译，缩放和翻译，以及相似变换。 为了解决连接问题，可变带宽平均偏移方法适用于估计GCC的最大似然解。
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