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    • 2. 发明授权
    • Image registration
    • 图像注册
    • US09053541B2
    • 2015-06-09
    • US13544192
    • 2012-07-09
    • Jim PiperSean MurphyIan PooleJian Song
    • Jim PiperSean MurphyIan PooleJian Song
    • G06K9/00G06T7/00
    • G06T7/0028G06T7/33G06T2207/30004
    • Certain embodiments provide a computer system operable to determine a registration mapping between a first medical image and a second medical image, the computer system comprising: a storage device for storing data representing the first medical image and the second medical image; and a processor unit operable to execute machine readable instructions to: (a) identify a plurality of elements in the first medical image; (b) determine a spatial mapping from each element in the first medical image to a corresponding element in the second medical image to provide a plurality of spatial mappings subject to a consistency constraint; and (c) determine a registration mapping between the first medical image and the second medical image based on the plurality of spatial mappings from the respective elements of the first medical image to the corresponding elements of the second medical image.
    • 某些实施例提供一种可操作以确定第一医学图像和第二医学图像之间的登记映射的计算机系统,所述计算机系统包括:存储装置,用于存储表示第一医学图像和第二医学图像的数据; 以及处理器单元,其可操作以执行机器可读指令以:(a)识别所述第一医学图像中的多个元素; (b)确定从所述第一医学图像中的每个元素到所述第二医学图像中的对应元件的空间映射,以提供经受一致性约束的多个空间映射; 以及(c)基于从第一医用图像的各个元件到第二医用图像的相应元件的多个空间映射,确定第一医用图像与第二医用图像之间的登记映射。
    • 3. 发明申请
    • IMAGE REGISTRATION
    • 图像注册
    • US20140010422A1
    • 2014-01-09
    • US13544192
    • 2012-07-09
    • Jim PiperSean MurphyIan PooleJian Song
    • Jim PiperSean MurphyIan PooleJian Song
    • G06K9/00
    • G06T7/0028G06T7/33G06T2207/30004
    • Certain embodiments provide a computer system operable to determine a registration mapping between a first medical image and a second medical image, the computer system comprising: a storage device for storing data representing the first medical image and the second medical image; and a processor unit operable to execute machine readable instructions to: (a) identify a plurality of elements in the first medical image; (b) determine a spatial mapping from each element in the first medical image to a corresponding element in the second medical image to provide a plurality of spatial mappings subject to a consistency constraint; and (c) determine a registration mapping between the first medical image and the second medical image based on the plurality of spatial mappings from the respective elements of the first medical image to the corresponding elements of the second medical image.
    • 某些实施例提供可操作以确定第一医学图像和第二医学图像之间的登记映射的计算机系统,所述计算机系统包括:存储装置,用于存储表示第一医学图像和第二医学图像的数据; 以及处理器单元,其可操作以执行机器可读指令以:(a)识别所述第一医学图像中的多个元素; (b)确定从所述第一医学图像中的每个元素到所述第二医学图像中的对应元件的空间映射,以提供经受一致性约束的多个空间映射; 以及(c)基于从第一医用图像的各个元件到第二医用图像的相应元件的多个空间映射,确定第一医用图像与第二医用图像之间的登记映射。
    • 4. 发明申请
    • IMAGE SEGMENTATION
    • 图像分割
    • US20120177263A1
    • 2012-07-12
    • US13407867
    • 2012-02-29
    • Akinola AKINYEMIIan PooleCostas PlakasJim Piper
    • Akinola AKINYEMIIan PooleCostas PlakasJim Piper
    • G06K9/00
    • G06T7/11G06T2207/20128G06T2207/30004Y10S128/922Y10S128/923Y10S128/924
    • 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.
    • 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。
    • 5. 发明授权
    • Processing of abdominal images
    • 腹部图像处理
    • US08644575B2
    • 2014-02-04
    • US13036414
    • 2011-02-28
    • Jim PiperIan Poole
    • Jim PiperIan Poole
    • G06K9/00
    • A61B6/507G06F19/00G06T7/30G06T2200/04G06T2207/10076G06T2207/10081G06T2207/30092
    • 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.
    • 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。
    • 6. 发明授权
    • Image segmentation
    • 图像分割
    • US08160357B2
    • 2012-04-17
    • US12847372
    • 2010-07-30
    • Akinola AkinyemiIan PooleCostas PlakasJim Piper
    • Akinola AkinyemiIan PooleCostas PlakasJim Piper
    • G06K9/34
    • G06T7/11G06T2207/20128G06T2207/30004Y10S128/922Y10S128/923Y10S128/924
    • 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.
    • 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。
    • 7. 发明授权
    • Image processing apparatus and method
    • 图像处理装置及方法
    • US08792729B2
    • 2014-07-29
    • US13537701
    • 2012-06-29
    • Lu BaiJun WanJing DaiShaobin WangJim PiperCostas Plakas
    • Lu BaiJun WanJing DaiShaobin WangJim PiperCostas Plakas
    • G06K9/62
    • G06T3/0068
    • An image processing apparatus may include: a first registration device for performing, by taking a first input image of two overlapped input images having an overlapped area as a reference image, a first registration on a second input image to find, in the second input image, a second pixel which is matched with each first pixel located in the overlapped area of the reference image; an output pixel location determination device for calculating a location of an output pixel which is located in the overlapped area of the output image and corresponds to the first pixel, the locations of the first and second pixels being respectively weighted, and the shorter the distance from the first pixel to a non-overlapped area of the reference image is, the greater a weight of the location of the first pixel is; and an output pixel value determination device for calculating a pixel value.
    • 图像处理装置可以包括:第一登记装置,用于通过将具有重叠区域的两个重叠输入图像的第一输入图像作为参考图像进行第一注册,以在第二输入图像中找到第一注册,以在第二输入图像 与位于参考图像的重叠区域中的每个第一像素匹配的第二像素; 输出像素位置确定装置,用于计算位于输出图像的重叠区域中并对应于第一像素的输出像素的位置,第一和第二像素的位置分别被加权,并且距离 参考图像的非重叠区域的第一像素是第一像素的位置的权重越大; 以及用于计算像素值的输出像素值确定装置。
    • 9. 发明授权
    • Image segmentation
    • 图像分割
    • US08411950B2
    • 2013-04-02
    • US13407867
    • 2012-02-29
    • Akinola AkinyemiIan PooleCostas PlakasJim Piper
    • Akinola AkinyemiIan PooleCostas PlakasJim Piper
    • G06K9/34
    • G06T7/11G06T2207/20128G06T2207/30004Y10S128/922Y10S128/923Y10S128/924
    • 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.
    • 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。
    • 10. 发明申请
    • IMAGE SEGMENTATION
    • 图像分割
    • US20120027272A1
    • 2012-02-02
    • US12847372
    • 2010-07-30
    • Akinola AkinyemiIan PooleCostas PlakasJim Piper
    • Akinola AkinyemiIan PooleCostas PlakasJim Piper
    • G06K9/34
    • G06T7/11G06T2207/20128G06T2207/30004Y10S128/922Y10S128/923Y10S128/924
    • 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.
    • 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。