公开(公告)号：US20120177263A1

公开(公告)日：2012-07-12

申请号：US13407867

申请日：2012-02-29

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： G06K9/00

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.

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。

公开(公告)号：US08411950B2

公开(公告)日：2013-04-02

申请号：US13407867

申请日：2012-02-29

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.

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。

公开(公告)号：US20120027272A1

公开(公告)日：2012-02-02

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。

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。

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。

公开(公告)号：US08792729B2

公开(公告)日：2014-07-29

申请号：US13537701

申请日：2012-06-29

申请人： Lu Bai ,  Jun Wan ,  Jing Dai ,  Shaobin Wang ,  Jim Piper ,  Costas Plakas

发明人： Lu Bai ,  Jun Wan ,  Jing Dai ,  Shaobin Wang ,  Jim Piper ,  Costas Plakas

IPC分类号： G06K9/62

CPC分类号： G06T3/0068

摘要： 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.

摘要翻译： 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。

公开(公告)号：US08958618B2

公开(公告)日：2015-02-17

申请号：US13535895

申请日：2012-06-28

申请人： Saad Masood ,  Brian Mohr ,  Costas Plakas

发明人： Saad Masood ,  Brian Mohr ,  Costas Plakas

IPC分类号： G06K9/00 ,  G06K9/34 ,  G06K9/62

CPC分类号： G06K9/624 ,  A61B6/032 ,  A61B6/481 ,  A61B6/504 ,  A61B6/5217 ,  G06F19/00 ,  G06K9/00214 ,  G06K9/38 ,  G06K9/50 ,  G06T7/0012 ,  G06T2207/10081 ,  G06T2207/30101

摘要： A computer implemented method identifying calcification in a patient image data set including blood vessels. The method includes: obtaining an image data set including voxels each having an intensity value; forming the intensity values into an intensity value group covering an intensity range; defining plural intensity thresholds across the intensity range and including its end values; for each intensity threshold, partitioning the intensity values into two sub-groups according to intensity threshold, and calculating an information criterion based on intensity threshold; generating an information criterion measure curve that plots the calculated information criteria against intensity threshold; locating a maximum in the information criterion measure curve and setting the corresponding intensity threshold as a calcification threshold; and partitioning the intensity values into two sub-groups using the calcification threshold, identifying voxels corresponding to intensity values in the sub-group above the calcification threshold as representing calcification in the patient.

摘要翻译： 一种在包括血管的患者图像数据集中识别钙化的计算机实现的方法。 该方法包括：获得包含各具有强度值的体素的图像数据集; 将强度值形成为覆盖强度范围的强度值组; 在强度范围内定义多个强度阈值并包括其终值; 对于每个强度阈值，根据强度阈值将强度值划分为两个子组，并且基于强度阈值计算信息准则; 生成信息准则测量曲线，绘制计算出的信息准则与强度阈值; 在信息标准测量曲线中定位最大值，并将相应的强度阈值设置为钙化阈值; 以及使用钙化阈值将强度值分成两个子组，识别对应于高于钙化阈值的子组中的强度值的体素表示患者的钙化。