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1. 发明授权

US08693750B2 Method and system for automatic detection of spinal bone lesions in 3D medical image data 有权
标题翻译： 3D医学图像数据中脊髓损伤自动检测方法及系统
公开(公告)号：US08693750B2
公开(公告)日：2014-04-08
申请号：US13342248
申请日：2012-01-03
申请人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62 , G06K9/68 , G06K9/70
CPC分类号： G06K9/4614 , G06K9/6256 , G06K2209/055 , G06K2209/40 , G06T7/0012 , G06T2207/10072 , G06T2207/20016 , G06T2207/20081 , G06T2207/30012 , G06T2207/30096
摘要： A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
摘要翻译：公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。在3D医学图像中检测与骨骼区域相对应的兴趣区域。使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。

2. 发明申请

US20120183193A1 Method and System for Automatic Detection of Spinal Bone Lesions in 3D Medical Image Data 有权
标题翻译：自动检测3D医学图像数据中脊髓损伤的方法和系统
公开(公告)号：US20120183193A1
公开(公告)日：2012-07-19
申请号：US13342248
申请日：2012-01-03
申请人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/4614 , G06K9/6256 , G06K2209/055 , G06K2209/40 , G06T7/0012 , G06T2207/10072 , G06T2207/20016 , G06T2207/20081 , G06T2207/30012 , G06T2207/30096
摘要： A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
摘要翻译：公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。在3D医学图像中检测与骨骼区域相对应的兴趣区域。使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。

3. 发明申请

US20120106810A1 Automated Rib Ordering and Pairing 有权
标题翻译：自动排序和配对
公开(公告)号：US20120106810A1
公开(公告)日：2012-05-03
申请号：US13274515
申请日：2011-10-17
申请人： Sowmya Ramakrishnan , Christopher V. Alvino , Dijia Wu , David Liu , Shaohua Kevin Zhou
发明人： Sowmya Ramakrishnan , Christopher V. Alvino , Dijia Wu , David Liu , Shaohua Kevin Zhou
IPC分类号： G06K9/00
CPC分类号： G06T7/73 , G06T2207/10081 , G06T2207/30012 , G06T2207/30064 , G06T2207/30172
摘要： Ribs are automatically ordered and paired. After ordering ribs on each side, magnetic and spring functions are used to solve for rib pairing. The magnetic function is used to constrain possible pairs across sides, and the spring function is used to maintain the order on each side while accounting for missing or fused ribs.
摘要翻译：肋骨自动排序并配对。在每侧排列肋后，使用磁力和弹簧功能来解决肋配对。磁功能用于限制双方的可能对，弹簧功能用于维护每侧的顺序，同时考虑到缺失或融合的肋骨。

4. 发明授权

US08571285B2 Automated rib ordering and pairing 有权
标题翻译：自动肋订购和配对
公开(公告)号：US08571285B2
公开(公告)日：2013-10-29
申请号：US13274515
申请日：2011-10-17
申请人： Sowmya Ramakrishnan , Christopher V. Alvino , Dijia Wu , David Liu , Shaohua Kevin Zhou
发明人： Sowmya Ramakrishnan , Christopher V. Alvino , Dijia Wu , David Liu , Shaohua Kevin Zhou
IPC分类号： G06K9/00 , A61B5/08
CPC分类号： G06T7/73 , G06T2207/10081 , G06T2207/30012 , G06T2207/30064 , G06T2207/30172
摘要： Ribs are automatically ordered and paired. After ordering ribs on each side, magnetic and spring functions are used to solve for rib pairing. The magnetic function is used to constrain possible pairs across sides, and the spring function is used to maintain the order on each side while accounting for missing or fused ribs.
摘要翻译：肋骨自动排序并配对。在每侧排列肋后，使用磁力和弹簧功能来解决肋配对。磁功能用于限制双方的可能对，弹簧功能用于维护每侧的顺序，同时考虑到缺失或融合的肋骨。

5. 发明申请

US20090041328A1 Feature Processing For Lung Nodules In Computer Assisted Diagnosis 有权
标题翻译：计算机辅助诊断中肺结节特征处理
公开(公告)号：US20090041328A1
公开(公告)日：2009-02-12
申请号：US12170639
申请日：2008-07-10
申请人： Lin Hong , Christopher V. Alvino , Hong Shen
发明人： Lin Hong , Christopher V. Alvino , Hong Shen
IPC分类号： G06K9/62
CPC分类号： G06T7/0012 , G06T2207/30061 , G06T2207/30064
摘要： Feature processing is provided for lung nodules in computer-assisted diagnosis. A feature that may better distinguish nodules from background is extracted using a Hough transform. Rather than relying on a specific boundary shape, the Hough transform accumulates evidence associated with a region, such as a ring region. The accumulated evidence provides a feature score without requiring a nodule to fit a specific shape. In another approach, a background level is determined from extracted features. Rather than attempting to normalize an image prior to extraction, the features are normalized. The feature normalization and generalized Hough transform extraction may be used together or alone.
摘要翻译：在计算机辅助诊断中为肺结节提供特征处理。使用霍夫变换提取可以更好地区分结节与背景的特征。霍夫变换不是依赖于特定的边界形状，而是累积与区域相关联的证据，例如环形区域。积累的证据提供了一个特征分数，而不需要结节来适应特定的形状。在另一种方法中，从提取的特征确定背景级别。在尝试在提取之前对图像进行归一化，而不是将特征归一化。特征归一化和广义霍夫变换提取可以一起使用或单独使用。

6. 发明授权

US08107699B2 Feature processing for lung nodules in computer assisted diagnosis 有权
标题翻译：计算机辅助诊断肺结节特征处理
公开(公告)号：US08107699B2
公开(公告)日：2012-01-31
申请号：US12170639
申请日：2008-07-10
申请人： Lin Hong , Christopher V. Alvino , Hong Shen
发明人： Lin Hong , Christopher V. Alvino , Hong Shen
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T2207/30061 , G06T2207/30064
摘要： Feature processing is provided for lung nodules in computer-assisted diagnosis. A feature that may better distinguish nodules from background is extracted using a Hough transform. Rather than relying on a specific boundary shape, the Hough transform accumulates evidence associated with a region, such as a ring region. The accumulated evidence provides a feature score without requiring a nodule to fit a specific shape. In another approach, a background level is determined from extracted features. Rather than attempting to normalize an image prior to extraction, the features are normalized. The feature normalization and generalized Hough transform extraction may be used together or alone.
摘要翻译：在计算机辅助诊断中为肺结节提供特征处理。使用霍夫变换提取可以更好地区分结节与背景的特征。霍夫变换不是依赖于特定的边界形状，而是累积与区域相关联的证据，例如环形区域。积累的证据提供了一个特征分数，而不需要结节来适应特定的形状。在另一种方法中，从提取的特征确定背景级别。在尝试在提取之前对图像进行归一化，而不是将特征归一化。特征归一化和广义霍夫变换提取可以一起使用或单独使用。

7. 发明申请

US20090116737A1 Machine Learning For Tissue Labeling Segmentation 有权
标题翻译：机器学习用于组织标签分割
公开(公告)号：US20090116737A1
公开(公告)日：2009-05-07
申请号：US12261383
申请日：2008-10-30
申请人： Atilla Peter Kiraly , Christopher V. Alvino , Hong Shen
发明人： Atilla Peter Kiraly , Christopher V. Alvino , Hong Shen
IPC分类号： G06K9/62
CPC分类号： G06K9/6269 , G06K9/0014 , G06T7/11 , G06T7/143 , G06T2207/30024 , G06T2207/30096
摘要： A method for directed machine learning includes receiving features including intensity data and location data of an image, condensing the intensity data and the location data into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image.
摘要翻译：用于定向机器学习的方法包括接收包括强度数据和图像的位置数据的特征，将强度数据和位置数据聚合成特征向量，通过多个分类器处理特征向量，每个分类器针对相应的训练类进行训练在多个类中，从每个分类器输出特征向量属于相应训练类的概率，并根据分类器的概率向特征向量分配标签，其中分配产生图像的分割。

8. 发明申请

US20100002925A1 Fluid Dynamics Approach To Image Segmentation 有权
标题翻译：流体动力学方法对图像分割
公开(公告)号：US20100002925A1
公开(公告)日：2010-01-07
申请号：US12496959
申请日：2009-07-02
申请人： Atilla Peter Kiraly , Leo Grady , Christopher V. Alvino
发明人： Atilla Peter Kiraly , Leo Grady , Christopher V. Alvino
IPC分类号： G06K9/34 , G06K9/00
CPC分类号： G06K9/342 , G06K2009/366 , G06T7/11 , G06T7/149 , G06T2207/10072 , G06T2207/10132 , G06T2207/20101 , G06T2207/30004
摘要： A method for segmenting image data within a data processing system includes acquiring an image. One or more seed points are established within the image. An advection vector field is computed based on image influences and user input. A dye concentration is determined at each of a plurality of portions of the image that results from a diffusion of dye within the computed advection field. The image is segmented into one or more regions based on the determined dye concentration for the corresponding dye.
摘要翻译：一种用于在数据处理系统内分割图像数据的方法包括获取图像。在图像内建立一个或多个种子点。基于图像影响和用户输入计算平流矢量场。在由计算的对流场内的染料扩散产生的图像的多个部分中的每一个处确定染料浓度。基于所确定的相应染料的染料浓度，将图像分割成一个或多个区域。

9. 发明申请

US20090190833A1 Piecewise Smooth Mumford-Shah on an Arbitrary Graph 有权
标题翻译：在任意图上分段平滑的Mumford-Shah
公开(公告)号：US20090190833A1
公开(公告)日：2009-07-30
申请号：US12362892
申请日：2009-01-30
申请人： Christopher V. Alvino , Leo Grady
发明人： Christopher V. Alvino , Leo Grady
IPC分类号： G06K9/40
CPC分类号： G06K9/40 , G06K9/6207
摘要： A method for recovering a contour using combinatorial optimization includes receiving an input image, initializing functions for gradient f, smooth background g, and contour r, determining an optimum of the gradient f of a region R in the input image, extending the optimum of the gradient f of region R to a complement of R, determining an optimum of the smooth background function g for a region Q corresponding to the complement of R, extending the optimum of the smooth background function g of region Q to a complement of Q, and determining an optimum contour r according to the optimum of the gradient f and the optimum of the smooth background function g.
摘要翻译：使用组合优化来恢复轮廓的方法包括：接收输入图像，初始化梯度f，平滑背景g和轮廓r的函数，确定输入图像中的区域R的渐变f的最佳值，区域R到R的补数的梯度f，确定对应于R的补码的区域Q的平滑背景函数g的最优，将区域Q的平滑背景函数g的最优值延伸到Q的补数，以及根据梯度f的最优值和平滑背景函数g的最优值确定最佳轮廓r。

10. 发明申请

US20080287796A1 Method and system for spine visualization in 3D medical images 有权
标题翻译： 3D医学图像中脊柱可视化的方法和系统
公开(公告)号：US20080287796A1
公开(公告)日：2008-11-20
申请号：US12150663
申请日：2008-04-30
申请人： Atilla Peter Kiraly , Christopher V. Alvino , Hong Shen
发明人： Atilla Peter Kiraly , Christopher V. Alvino , Hong Shen
IPC分类号： A61B8/00 , G06K9/00 , G06K9/32
CPC分类号： A61B5/103 , A61B5/055 , A61B5/407 , A61B5/4561 , A61B8/00 , G06K2209/055 , G06T7/13 , G06T7/155 , G06T7/60 , G06T15/08 , G06T2207/20044 , G06T2207/30012 , G06T2207/30172 , G06T2215/06
摘要： A method and system for visualizing the spine in 3D medical images is disclosed. A spinal cord centerline is automatically determined in a 3D medical image volume, such as a CT volume. A reformatted image volume is then generated based on the spinal cord centerline. The reformatted image volume can be a straightened spine volume or a Multi-planar Reconstruction (MPR) based volume that follows the natural curve of the spine. The reconstructed volume can be displayed as 2D slices or 3D volume renderings.
摘要翻译：公开了用于在3D医学图像中可视化脊柱的方法和系统。在3D医学图像体积（例如CT体积）中自动确定脊髓中心线。然后基于脊髓中心线产生重新格式化的图像体积。重新格式化的图像体积可以是矫正的脊柱体积或基于脊柱的自然曲线的基于多平面重建（MPR）的体积。重建体积可以显示为2D切片或3D体积渲染。

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