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    Displaying image data using automatic presets
    1.
    发明授权
    Displaying image data using automatic presets 有权
    使用自动预设显示图像数据

    公开(公告)号：US06658080B1

    公开(公告)日：2003-12-02

    申请号：US10212363

    申请日：2002-08-05

    申请人： Ian Poole ,  Andrew John Bissell

    发明人： Ian Poole ,  Andrew John Bissell

    IPC分类号： A61B603

    CPC分类号： G06T19/20 ,  A61B5/7445 ,  A61B6/466 ,  A61B8/461 ,  G06F19/00 ,  G06T7/12 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/30101 ,  G06T2210/41 ,  G06T2219/2012

    摘要： A computer automated method for setting visualization parameter boundaries in a preset for displaying an image from a 3D data set applicable to magnetic resonance (MR) data, computer tomography (CT) data and other 3D data sets obtained in medical imaging is described. In one example the visualization parameter boundaries are color boundaries. A histogram of data values of voxels within a user-selected volume of interest (VOI) is generated and an analysis of a convex hull spanning the histogram is made to provide one or more visualization thresholds which divide the histogram into sub-regions. The sub-regions relate to different tissue types within the VOI and color boundaries are set based on the visualization thresholds for displaying the different tissue types in different colors. The method allows color boundaries in a preset to be set objectively and automatically so that images can be displayed consistently and with less user manipulation. The method may also provide a measure of the significance of each color boundary in the preset to assist a user in interpreting a displayed image.

    摘要翻译： 描述了用于在可用于显示来自适用于医学成像中获得的磁共振（MR）数据，计算机断层摄影（CT）数据和其他3D数据集的3D数据集的图像的预设中设置可视化参数边界的计算机自动化方法。 在一个示例中，可视化参数边界是颜色边界。 生成用户选择的感兴趣体积（VOI）内的体素的数据值的直方图，并且对跨越直方图的凸包进行分析，以提供将直方图划分为子区域的一个或多个可视化阈值。 子区域涉及VOI内的不同组织类型，并且基于用于以不同颜色显示不同组织类型的可视化阈值设置颜色边界。 该方法允许客观和自动地设置预设中的颜色边界，使得可以一致地显示图像并且以较少的用户操纵来显示图像。 该方法还可以提供预设中每个颜色边界的重要性的量度，以帮助用户解释所显示的图像。

    Method for determining a path along a biological object with a lumen
    2.
    发明授权
    Method for determining a path along a biological object with a lumen 有权
    用于确定沿着具有内腔的生物体的路径的方法

    公开(公告)号：US07379062B2

    公开(公告)日：2008-05-27

    申请号：US11194516

    申请日：2005-08-01

    申请人： Ian Poole

    发明人： Ian Poole

    IPC分类号： G06T17/00 ,  G06K9/00 ,  A61B6/00

    CPC分类号： G06T7/60 ,  G06T7/12 ,  G06T7/181 ,  G06T2207/10072 ,  G06T2207/20101 ,  G06T2207/30101

    摘要： A path between specified start and end voxels along a biological object with a lumen, such as a vessel, within a patient image three-dimensional volume data set comprising an array of voxels of varying value is identified using an algorithm that works outwards from the start voxel to identify paths of low cost via intermediate voxels. The intermediate voxels are queued for further expansion of the path using a priority function comprising the sum of the cost of the path already found from the start voxel to the intermediate voxel and the Euclidean distance from the intermediate voxel to the end voxel. A cost function that depends on the voxel density is used to bias the algorithm towards paths inside the object. The number of iterations of the voxel required to find a path from the start to the end voxel, and hence the time taken, can be significantly reduced by scaling the Euclidean distance by a constant. Usefully, the constant is greater than 1, such as between 1.5 and 2.

    摘要翻译： 使用从开始向外工作的算法来识别包括具有变化值的体素阵列的患者图像三维体数据集内的具有管腔（例如血管）的生物体的指定起始和终止体素之间的路径 体素通过中间体素识别低成本的路径。 使用包括已经从起始体素到中间体素已经发现的路径的总和的总和的优先级函数以及从中间体素到终体体素的欧几里德距离来排列中间体素用于路径的进一步扩展。 使用取决于体素密度的成本函数将算法偏向对象内的路径。 通过将欧几里德距离缩放常数，可以显着减少从开始到结束体素找到路径所需的体素的迭代次数以及所花费的时间。 有用地，常数大于1，例如在1.5和2之间。

    VIRTUAL ENDOSCOPY
    4.
    发明申请
    VIRTUAL ENDOSCOPY 审中-公开
    虚拟内窥镜

    公开(公告)号：US20080117210A1

    公开(公告)日：2008-05-22

    申请号：US11562744

    申请日：2006-11-22

    申请人： Marco Razeto ,  Ian Poole

    发明人： Marco Razeto ,  Ian Poole

    IPC分类号： G06T17/00

    CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/73 ,  G06T19/003 ,  G06T2207/10081 ,  G06T2207/30028

    摘要： A method for automatically determining a start or finish location near to an end of a lumen in a medical image data set is described. The location may thus be used in determining a camera path for virtual endoscopy (e.g. colonoscopy). The data set comprises a plurality of voxels arranged along first, second and third directions and the method includes: segmenting the data set to identity a group of voxels classified as belonging to the lumen; selecting one of the axes of the data set as a primary direction based on an expected direction of the lumen at the end of interest; selecting a slice through the data set which is perpendicular to the primary direction and includes voxels at a spatial extremity of the group of voxels classified as belonging to the lumen along this direction; identifying a two-dimensional (2D) region of the voxels classified as belonging to the lumen within the selected slice; and selecting, based on the position of the 2D region, a position within the data set as the terminal location for the virtual colonoscopy camera path.

    摘要翻译： 描述了用于自动确定医疗图像数据集中的管腔末端附近的开始或完成位置的方法。 因此，该位置可以用于确定用于虚拟内窥镜检查（例如结肠镜检查）的相机路径。 所述数据集包括沿着第一，第二和第三方向布置的多个体素，并且所述方法包括：分割所述数据集以识别归类为所述管腔的一组体素; 基于感兴趣的结束时的管腔的预期方向，选择数据集的一个轴作为主要方向; 通过垂直于主方向的数据集选择切片，并且包括沿着该方向分类为属于内腔的体素组的空间末端的体素; 识别分类为属于所选切片内的内腔的体素的二维（2D）区域; 以及基于所述2D区域的位置来选择所述数据组内的位置作为所述虚拟结肠镜检查摄像机路径的终端位置。

    Feature location method and system
    5.
    发明授权
    Feature location method and system 有权
    特征定位方法和系统

    公开(公告)号：US08837791B2

    公开(公告)日：2014-09-16

    申请号：US12976725

    申请日：2010-12-22

    申请人： Costas Plakas ,  Ian Poole

    发明人： Costas Plakas ,  Ian Poole

    IPC分类号： G06K9/00 ,  G06T7/00

    CPC分类号： G06T7/0038 ,  G06K2209/05 ,  G06T7/38 ,  G06T7/74 ,  G06T2207/10081 ,  G06T2207/30004

    摘要： A method of locating anatomical features in a medical imaging dataset comprises obtaining a medical imaging measurement dataset that comprises image data for a subject body as a function of position; and performing a registration procedure that comprises:—providing a mapping between positions in the measurement dataset and positions in a reference dataset, wherein the reference dataset comprises reference image data for a reference body as a function of position, the reference dataset comprises at least one anatomical landmark, and the or each anatomical landmark is indicative of the position of a respective anatomical feature of the reference body; matching image data in the measurement dataset with image data for corresponding positions in the reference dataset, wherein the corresponding positions are determined according to the mapping; determining a measure of the match between the image data of the measurement dataset and the image data of the reference dataset; varying the mapping to improve the match between the image data of the measurement dataset and the image data of the reference dataset, thereby to obtain a registration mapping; and using the registration mapping to map the positions of the anatomical landmarks to positions in the measurement dataset, thereby to assign positions to anatomical features in the measurement dataset.

    摘要翻译： 一种在医学成像数据集中定位解剖特征的方法包括获得包括作为位置的函数的被摄体的图像数据的医学成像测量数据集; 并且执行注册过程，其包括：提供测量数据集中的位置和参考数据集中的位置之间的映射，其中所述参考数据集包括作为位置的函数的参考主体的参考图像数据，所述参考数据集包括至少一个 解剖标记，并且所述或每个解剖标记指示参考体的相应解剖特征的位置; 将测量数据集中的图像数据与参考数据集中的对应位置的图像数据进行匹配，其中根据映射确定相应的位置; 确定测量数据集的图像数据与参考数据集的图像数据之间的匹配的度量; 改变映射以改善测量数据集的图像数据与参考数据集的图像数据之间的匹配，从而获得注册映射; 并且使用所述配准映射将所述解剖学标记的位置映射到所述测量数据集中的位置，从而为所述测量数据集中的解剖特征分配位置。

    Processing of abdominal images
    6.
    发明授权
    Processing of abdominal images 有权
    腹部图像处理

    公开(公告)号：US08644575B2

    公开(公告)日：2014-02-04

    申请号：US13036414

    申请日：2011-02-28

    申请人： Jim Piper ,  Ian Poole

    发明人： Jim Piper ,  Ian Poole

    IPC分类号： G06K9/00

    CPC分类号： A61B6/507 ,  G06F19/00 ,  G06T7/30 ,  G06T2200/04 ,  G06T2207/10076 ,  G06T2207/10081 ,  G06T2207/30092

    摘要： According to one embodiment there is provided a computer-automated image processing method applied to a four-dimensional (4D) image data set of a patient's abdomen, e.g. by dynamic contrast enhanced computer-assisted tomography (DCE-CT). One of the three-dimensional (3D) scan images is taken to as the reference volume and the others as target volumes. Before registration between the 3D scan images, the image data set is partitioned into an abdominal cavity domain, containing the organs inside the abdominal wall, and an abdominal wall domain including the abdominal wall and externally adjacent skeletal features, such as the spine and ribs. Registration is then carried out separately on the two domains to obtain two warp fields which are then merged into a 4D image data set of the whole volume for further use, which may be to carry out perfusion measurements, to display and to store the registered 4D image data set.

    摘要翻译： 根据一个实施例，提供了一种应用于患者腹部的四维（4D）图像数据集的计算机自动化图像处理方法，例如， 通过动态对比增强计算机辅助断层扫描（DCE-CT）。 将三维（3D）扫描图像中的一个作为参考体积，其余的作为目标体积。 在3D扫描图像之前，将图像数据组分割成腹腔结构域，其中包含腹壁内的器官，以及包括腹壁和外部骨骼特征（例如脊柱和肋骨）的腹壁结构域。 然后在两个域上单独进行登记，以获得两个经线域，然后将它们合并到整个体积的4D图像数据集中供进一步使用，这可以进行灌注测量，以显示和存储注册的4D 图像数据集。

    Image segmentation
    9.
    发明授权
    Image segmentation 有权
    图像分割

    公开(公告)号：US08160357B2

    公开(公告)日：2012-04-17

    申请号：US12847372

    申请日：2010-07-30

    申请人： Akinola Akinyemi ,  Ian Poole ,  Costas Plakas ,  Jim Piper

    发明人： Akinola Akinyemi ,  Ian Poole ,  Costas Plakas ,  Jim Piper

    IPC分类号： G06K9/34

    CPC分类号： G06T7/11 ,  G06T2207/20128 ,  G06T2207/30004 ,  Y10S128/922 ,  Y10S128/923 ,  Y10S128/924

    摘要： According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable. The M atlases are then selected from among all the N candidate atlases to form the multi-atlas data set, the M atlases being those atlases determined to collectively provide the highest aggregate overlap over all the training data image sets.

    摘要翻译： 根据一个实施例，提供了一种从较大组的N个候选地图集中选择多个M个遗传数据的方法，以形成用于新颖图像数据集的计算机自动分割以标记感兴趣的对象的多图谱数据集 其中。 使用一组候选地图集，其中包含参考图像数据集和分割数据。 候选地图集中的每一个都按照一个一个出发的策略与其他地图集分割，其中候选地图集被用作彼此的训练数据。 依次对每个候选图集进行以下操作：注册; 分段; 计算重叠; 计算每个注册的相似性度量的值; 以及通过使用所述相似性度量作为所述独立变量进行回归并且所述重叠是因变量来获得一组回归参数。 然后，从所有N个候选地图集中选出M个图集以形成多图集数据集，M个图集被确定为在所有训练数据图像集上统一提供最高的聚集重叠。