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1. 发明授权

US08160357B2 Image segmentation 有权
标题翻译：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。

2. 发明授权

US08411950B2 Image segmentation 有权
标题翻译：图像分割
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。

3. 发明申请

US20120027272A1 IMAGE SEGMENTATION 有权
标题翻译：图像分割
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。

4. 发明授权

US07941462B2 Method and apparatus for classification of coronary artery image data 有权
标题翻译：用于冠状动脉图像数据分类的方法和装置
公开(公告)号：US07941462B2
公开(公告)日：2011-05-10
申请号：US12236789
申请日：2008-09-24
申请人： Akinola Akinyemi , Sean Murphy , Ian Poole
发明人： Akinola Akinyemi , Sean Murphy , Ian Poole
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06K9/469 , G06F19/00 , G06K9/6278 , G06K9/6282 , G06K2209/05 , G06T7/12 , G06T7/162 , G06T2207/30101 , G16H50/20 , G16H50/70
摘要： A polyline tree representation of a coronary artery tree imaged in a volume data set is obtained, and its topology is extracted to give a topological representation indicating the relative positions of vessels in the tree. The topological representation is compared with a set of topological rules to find possible anatomical classifications for each vessel, and a set of candidate labeled polyline trees is generated by labeling the polyline tree with labels showing each combination of possible anatomical classifications. Each candidate labeled tree is filtered according to a set of geometric rules pertaining to spatial characteristics of vessels in arterial trees, and any labeled tree not satisfying the geometric rules is rejected A figure of merit is calculated for each remaining candidate by comparing features of the vessels measured from the polyline tree and from the volume data set with features of correctly classified vessels in other data sets to determine the probable correctness of the labeling of each candidate, and the candidate with the best figure of merit is selected as showing the proper classification of the vessels.
摘要翻译：获得在体数据集中成像的冠状动脉树的折线图表示，并且提取其拓扑以给出指示树中血管的相对位置的拓扑表示。将拓扑表示与一组拓扑规则进行比较，以找到每个血管的可能的解剖学分类，并且通过用显示可能的解剖学分类的每个组合的标记来标记折线树生成一组候选标记的折线树。每个候选标签树根据与动脉树中血管的空间特征相关的一组几何规则进行过滤，并且任何不符合几何规则的标记树被拒绝通过比较血管的特征来计算每个剩余候选人的品质因数从折线树和具有其他数据集中正确分类血管特征的体积数据集测量，以确定每个候选人的标签的可能正确性，并选择具有最佳品质因数的候选者，以显示适当的分类船只。

5. 发明申请

US20100082692A1 METHOD AND APPARATUS FOR CLASSIFICATION OF CORONARY ARTERY IMAGE DATA 有权
标题翻译：冠状动脉图像数据分类的方法和装置
公开(公告)号：US20100082692A1
公开(公告)日：2010-04-01
申请号：US12236789
申请日：2008-09-24
申请人： Akinola Akinyemi , Sean Murphy , Ian Poole
发明人： Akinola Akinyemi , Sean Murphy , Ian Poole
IPC分类号： G06F17/30
CPC分类号： G06K9/469 , G06F19/00 , G06K9/6278 , G06K9/6282 , G06K2209/05 , G06T7/12 , G06T7/162 , G06T2207/30101 , G16H50/20 , G16H50/70
摘要： A polyline tree representation of a coronary artery tree imaged in a volume data set is obtained, and its topology is extracted to give a topological representation indicating the relative positions of vessels in the tree. The topological representation is compared with a set of topological rules to find possible anatomical classifications for each vessel, and a set of candidate labeled polyline trees is generated by labeling the polyline tree with labels showing each combination of possible anatomical classifications. Each candidate labeled tree is filtered according to a set of geometric rules pertaining to spatial characteristics of vessels in arterial trees, and any labeled tree not satisfying the geometric rules is rejected A figure of merit is calculated for each remaining candidate by comparing features of the vessels measured from the polyline tree and from the volume data set with features of correctly classified vessels in other data sets to determine the probable correctness of the labeling of each candidate, and the candidate with the best figure of merit is selected as showing the proper classification of the vessels.
摘要翻译：获得在体数据集中成像的冠状动脉树的折线图表示，并且提取其拓扑以给出指示树中血管的相对位置的拓扑表示。将拓扑表示与一组拓扑规则进行比较，以找到每个血管的可能的解剖学分类，并且通过用显示可能的解剖学分类的每个组合的标记来标记折线树生成一组候选标记的折线树。每个候选标签树根据与动脉树中血管的空间特征相关的一组几何规则进行过滤，并且任何不符合几何规则的标记树被拒绝通过比较血管的特征来计算每个剩余候选人的品质因数从折线树和具有其他数据集中正确分类血管特征的体积数据集测量，以确定每个候选人的标签的可能正确性，并选择具有最佳品质因数的候选者，以显示适当的分类船只。

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