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1. 发明授权

US08213700B2 Systems and methods for identifying suspicious anomalies using information from a plurality of images of an anatomical colon under study 有权
标题翻译：使用来自正在研究的解剖结肠的多个图像的信息来识别可疑异常的系统和方法
公开(公告)号：US08213700B2
公开(公告)日：2012-07-03
申请号：US12415664
申请日：2009-03-31
申请人： Senthil Periaswamy , Michael J. Collins , Ryan McGinnis , Robert L. Van Uitert
发明人： Senthil Periaswamy , Michael J. Collins , Ryan McGinnis , Robert L. Van Uitert
IPC分类号： G06K9/00
CPC分类号： G06K9/00 , G06K2209/053 , G06T7/0012 , G06T2207/10081 , G06T2207/10088 , G06T2207/30032
摘要： Systems, computer-readable media, and methods are presented that identify suspicious anomalies in a colon with higher sensitivity and at a lower false positive rate. A plurality of images of an anatomical colon is acquired. Candidate suspicious anomalies are identified in each image. The candidate suspicious anomalies across images are then compared using registration and matching. Features of candidate suspicious anomalies across images may be jointly evaluated to perform classification.
摘要翻译：提出了系统，计算机可读介质和方法，其以更高的灵敏度和更低的假阳性率鉴定结肠中的可疑异常。获取解剖结肠的多个图像。每个图像中都有可疑的可疑异常。然后通过注册和匹配来比较跨图像的候选可疑异常。跨图像的候选可疑异常的特征可以联合评估以进行分类。

2. 发明申请

US20100246912A1 SYSTEMS AND METHODS FOR IDENTIFYING SUSPICIOUS ANOMALIES USING INFORMATION FROM A PLURALITY OF IMAGES OF AN ANATOMICAL COLON UNDER STUDY 有权
标题翻译：使用来自研究中的解剖学图像的多个图像的信息来识别可疑异常的系统和方法
公开(公告)号：US20100246912A1
公开(公告)日：2010-09-30
申请号：US12415664
申请日：2009-03-31
申请人： Senthil Periaswamy , Michael J. Collins , Ryan McGinnis , Robert L. Van Uitert
发明人： Senthil Periaswamy , Michael J. Collins , Ryan McGinnis , Robert L. Van Uitert
IPC分类号： G06K9/00 , G06K9/32 , G06K9/62
CPC分类号： G06K9/00 , G06K2209/053 , G06T7/0012 , G06T2207/10081 , G06T2207/10088 , G06T2207/30032
摘要： Systems, computer-readable media, and methods are presented that identify suspicious anomalies in a colon with higher sensitivity and at a lower false positive rate. A plurality of images of an anatomical colon is acquired. Candidate suspicious anomalies are identified in each image. The candidate suspicious anomalies across images are then compared using registration and matching. Features of candidate suspicious anomalies across images may be jointly evaluated to perform classification.
摘要翻译：提出了系统，计算机可读介质和方法，其以更高的灵敏度和更低的假阳性率鉴定结肠中的可疑异常。获取解剖结肠的多个图像。每个图像中都有可疑的可疑异常。然后通过注册和匹配来比较跨图像的候选可疑异常。跨图像的候选可疑异常的特征可以联合评估以进行分类。

3. 发明申请

US20100128950A1 Computer-Aided Detection of Regions of Interest in Tomographic Breast Imagery 有权
标题翻译：计算机辅助检测断层乳腺成像中的感兴趣区域
公开(公告)号：US20100128950A1
公开(公告)日：2010-05-27
申请号：US12579898
申请日：2009-10-15
申请人： Kevin Woods , Michael J. Collins , Senthil Periaswamy , Robert L. Van Uitert
发明人： Kevin Woods , Michael J. Collins , Senthil Periaswamy , Robert L. Van Uitert
IPC分类号： G06K9/00 , H05G1/60
CPC分类号： A61B6/5217 , A61B6/03 , A61B6/502 , G06K9/3233 , G06K2209/053 , G06T7/0012 , G06T2207/10072 , G06T2207/10124 , G06T2207/20104 , G06T2207/20108 , G06T2207/30068
摘要： Disclosed are methods, and associated systems comprising processors, input devices and output devices, of detecting regions of interest in a tomographic breast image. The methods may comprise: acquiring tomographic breast image data; deriving a plurality of synthetic sub-volumes from the tomographic breast image data; wherein each subvolume is defined by parallel planar top and bottom surfaces; wherein planar top and bottom surfaces of successive subvolumes are parallel to each other; and wherein a top planar surface of a sub-volume is offset from a top planar surface of a prior sub-volume, such that successive sub-volumes overlap; for each sub-volume, deriving a two-dimensional image; for each two-dimensional image, identifying regions of interest therein; deriving at least one region of interest of potential clinical interest from a plurality of identified regions of interest; and outputting information associated with at least one derived region of interest of potential clinical interest.
摘要翻译：公开了包括处理器，输入设备和输出设备的方法和相关系统，其检测断层图像乳房图像中的感兴趣区域。所述方法可以包括：获取断层摄影乳房图像数据; 从断层图像乳房图像数据中导出多个合成子体积; 其中每个子体积由平行的平面顶部和底部表面限定; 其中连续子体积的平面顶表面和底表面彼此平行; 并且其中子体积的顶部平坦表面偏离先前子体积的顶部平坦表面，使得连续的子体积重叠; 对于每个子体积，导出二维图像; 对于每个二维图像，识别其中感兴趣的区域; 从多个确定的感兴趣区域中产生潜在临床兴趣的至少一个感兴趣区域; 并输出与潜在临床感兴趣的至少一个感兴趣的衍生区域相关联的信息。

4. 发明授权

US08634622B2 Computer-aided detection of regions of interest in tomographic breast imagery 有权
标题翻译：计算机辅助检测断层摄影乳腺成像感兴趣区域
公开(公告)号：US08634622B2
公开(公告)日：2014-01-21
申请号：US12579898
申请日：2009-10-15
申请人： Kevin Woods , Michael J. Collins , Senthil Periaswamy , Robert L. Van Uitert
发明人： Kevin Woods , Michael J. Collins , Senthil Periaswamy , Robert L. Van Uitert
IPC分类号： A61B6/03
CPC分类号： A61B6/5217 , A61B6/03 , A61B6/502 , G06K9/3233 , G06K2209/053 , G06T7/0012 , G06T2207/10072 , G06T2207/10124 , G06T2207/20104 , G06T2207/20108 , G06T2207/30068
摘要： Disclosed are methods, and associated systems comprising processors, input devices and output devices, of detecting regions of interest in a tomographic breast image. The methods may comprise: acquiring tomographic breast image data; deriving a plurality of synthetic sub-volumes from the tomographic breast image data; wherein each subvolume is defined by parallel planar top and bottom surfaces; wherein planar top and bottom surfaces of successive subvolumes are parallel to each other; and wherein a top planar surface of a sub-volume is offset from a top planar surface of a prior sub-volume, such that successive sub-volumes overlap; for each sub-volume, deriving a two-dimensional image; for each two-dimensional image, identifying regions of interest therein; deriving at least one region of interest of potential clinical interest from a plurality of identified regions of interest; and outputting information associated with at least one derived region of interest of potential clinical interest.
摘要翻译：公开了包括处理器，输入设备和输出设备的方法和相关系统，其检测断层图像乳房图像中的感兴趣区域。所述方法可以包括：获取断层摄影乳房图像数据; 从断层图像乳房图像数据中导出多个合成子体积; 其中每个子体积由平行的平面顶部和底部表面限定; 其中连续子体积的平面顶表面和底表面彼此平行; 并且其中子体积的顶部平坦表面偏离先前子体积的顶部平坦表面，使得连续的子体积重叠; 对于每个子体积，导出二维图像; 对于每个二维图像，识别其中感兴趣的区域; 从多个确定的感兴趣区域中产生潜在临床兴趣的至少一个感兴趣区域; 并输出与潜在临床感兴趣的至少一个感兴趣的衍生区域相关联的信息。

5. 发明授权

US08131036B2 Computer-aided detection and display of colonic residue in medical imagery of the colon 有权
标题翻译：计算机辅助检测和结肠残留物在结肠医学影像中的显示
公开(公告)号：US08131036B2
公开(公告)日：2012-03-06
申请号：US12179787
申请日：2008-07-25
申请人： Michael J. Collins , Senthil Periaswamy , Robert V. Uitert , Justin Kunz
发明人： Michael J. Collins , Senthil Periaswamy , Robert V. Uitert , Justin Kunz
IPC分类号： G06K9/00
CPC分类号： G06K9/00 , G06K2209/053
摘要： Methods and systems are presented that improve a radiologist's ability to identify polyps by automatically and more accurately detecting and displaying colonic residue such as tagged or untagged stool or colonic fluid in medical images of the colorectal region. A virtual colonography imaging system obtains medical imagery of the colon. Improved computer-aided detection (CAD) algorithms identify colonic residue in the imagery by calculating feature vectors of and using statistical classification methods to classify regions of colonic residue to distinguish them from false positives.
摘要翻译：提出了通过自动更准确地检测和显示结肠直肠区域的医学图像中的标记或未标记的粪便或结肠液的结肠残留物来改善放射科医师识别息肉的能力的方法和系统。虚拟结肠成像系统获得结肠的医学图像。改进的计算机辅助检测（CAD）算法通过计算特征向量并使用统计分类方法对结肠残留区域进行分类，从而将其与假阳性区分开来，从而识别图像中的结肠残留。

6. 发明申请

US20100021026A1 COMPUTER-AIDED DETECTION AND DISPLAY OF COLONIC RESIDUE IN MEDICAL IMAGERY OF THE COLON 有权
标题翻译：计算机辅助检测和显示在殖民地医学图像中的残留
公开(公告)号：US20100021026A1
公开(公告)日：2010-01-28
申请号：US12179787
申请日：2008-07-25
申请人： Michael J. Collins , Senthil Periaswamy , Robert V. Uitert , Justin Kunz
发明人： Michael J. Collins , Senthil Periaswamy , Robert V. Uitert , Justin Kunz
IPC分类号： G06K9/00
CPC分类号： G06K9/00 , G06K2209/053
摘要： Methods and systems are presented that improve a radiologist's ability to identify polyps by automatically and more accurately detecting and displaying colonic residue such as tagged or untagged stool or colonic fluid in medical images of the colorectal region. A virtual colonography imaging system obtains medical imagery of the colon. Improved computer-aided detection (CAD) algorithms identify colonic residue in the imagery by calculating feature vectors of and using statistical classification methods to classify regions of colonic residue to distinguish them from false positives.
摘要翻译：提出了通过自动更准确地检测和显示结肠直肠区域的医学图像中的标记或未标记的粪便或结肠液的结肠残留物来改善放射科医师识别息肉的能力的方法和系统。虚拟结肠成像系统获得结肠的医学图像。改进的计算机辅助检测（CAD）算法通过计算特征向量并使用统计分类方法对结肠残留区域进行分类，从而将其与假阳性区分开来，从而识别图像中的结肠残留。

7. 发明授权

US08175351B2 Computer-aided detection and classification of suspicious masses in breast imagery 有权
标题翻译：计算机辅助检测和乳腺图像中可疑肿块的分类
公开(公告)号：US08175351B2
公开(公告)日：2012-05-08
申请号：US12211593
申请日：2008-09-16
申请人： Michael J. Collins , Kevin Woods , Brent Woods , William Pierson , Ryan McGinnis
发明人： Michael J. Collins , Kevin Woods , Brent Woods , William Pierson , Ryan McGinnis
IPC分类号： G06K9/00 , G06K9/62 , A61B6/04
CPC分类号： G06K9/6292 , G06K2209/053 , G06T7/0012 , G06T2207/30068
摘要： Methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.
摘要翻译：提出了以高灵敏度和可接受的假阳性率检测和分类显示具有螺旋和/或致密特征的质粒状区域的方法。在乳房的医学图像中识别出一个或多个可疑群体。在某些实施方案中，针对位于每个可疑质量计算密度的定量测量和密度的定量测量。然后，根据这两种定量方法，选择至少一种使用具有相似量化措施的真假假阳性分类方案。在某些其他实施例中，针对每个可疑质量计算乳房位置的度量。在一个实施例中，该位置确定可疑质量是否出现在乳房的实质区域的内部或外部。

8. 发明申请

US20100067754A1 COMPUTER-AIDED DETECTION AND CLASSIFICATION OF SUSPICIOUS MASSES IN BREAST IMAGERY 有权
标题翻译：乳腺成像中计算机辅助检测和分类的可疑质量
公开(公告)号：US20100067754A1
公开(公告)日：2010-03-18
申请号：US12211593
申请日：2008-09-16
申请人： Michael J. Collins , Kevin Woods , Brent Woods , William Pierson , Ryan McGinnis
发明人： Michael J. Collins , Kevin Woods , Brent Woods , William Pierson , Ryan McGinnis
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/6292 , G06K2209/053 , G06T7/0012 , G06T2207/30068
摘要： Methods, a system, and a computer readable medium are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In accordance with certain embodiments, for each suspicious mass located, a quantitative measure of spiculation and quantitative measure of density are computed. At least one classification scheme is then selected for each suspicious mass according to both quantitative measures. Each classification scheme is developed using true positives and false positives with similar quantitative measures.In accordance with certain other embodiments, for each suspicious mass located, a measure of breast location is computed. At least one classification scheme is then selected for each suspicious mass according to the measure of breast location. Each classification scheme is developed using true positives and false positives that appear in the same breast location. In one embodiment, the location measure determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.
摘要翻译：提出了方法，系统和计算机可读介质，其以高灵敏度和可接受的假阳性率检测和分类显示具有螺旋和/或密度特征的质量样区域。在乳房的医学图像中识别出一个或多个可疑群体。根据某些实施例，对于每个可疑的质量位置，计算密度的定量测量和密度的定量测量。然后根据这两种量化措施，为每个可疑物质选择至少一种分类方案。每个分类方案是使用具有相似量化措施的真阳性和假阳性来开发的。根据某些其他实施例，对于位于每个可疑质量块，计算乳房位置的量度。然后根据乳房位置的测量，为每个可疑质量选择至少一个分类方案。每个分类方案使用出现在同一乳房位置的真阳性和假阳性进行开发。在一个实施例中，位置测量确定可疑质量是否出现在乳房的实质区域的内部或外部。

9. 发明授权

US08687867B1 Computer-aided detection and classification of suspicious masses in breast imagery 有权
标题翻译：计算机辅助检测和乳腺图像中可疑肿块的分类
公开(公告)号：US08687867B1
公开(公告)日：2014-04-01
申请号：US13465808
申请日：2012-05-07
申请人： Michael J. Collins , Kevin Woods , Brent Woods , William Pierson , Ryan McGinnis
发明人： Michael J. Collins , Kevin Woods , Brent Woods , William Pierson , Ryan McGinnis
IPC分类号： G06K9/00 , A61B6/04 , G01N23/00
CPC分类号： G06K9/6292 , G06K2209/053 , G06T7/0012 , G06T2207/30068
摘要： Systems and methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.
摘要翻译：提出了系统和方法，其以高灵敏度和可接受的假阳性率检测和分类显示具有螺旋和/或密集特征的质量样区域。在乳房的医学图像中识别出一个或多个可疑群体。在某些实施方案中，针对位于每个可疑质量计算密度的定量测量和密度的定量测量。然后，根据这两种量化方法，选择至少一种使用具有相似量化措施的真假假阳性分类方案。在某些其他实施例中，针对每个可疑质量计算乳房位置的度量。在一个实施例中，该位置确定可疑质量是否出现在乳房的实质区域的内部或外部。

10. 发明申请

US20110026791A1 SYSTEMS, COMPUTER-READABLE MEDIA, AND METHODS FOR CLASSIFYING AND DISPLAYING BREAST DENSITY 审中-公开
标题翻译：系统，计算机可读介质和分类和显示乳腺密度的方法
公开(公告)号：US20110026791A1
公开(公告)日：2011-02-03
申请号：US12533952
申请日：2009-07-31
申请人： Michael J. Collins , Hrishikesh Haldankar , Brent Woods , Ryan McGinnis , Kevin Woods
发明人： Michael J. Collins , Hrishikesh Haldankar , Brent Woods , Ryan McGinnis , Kevin Woods
IPC分类号： G06K9/00
CPC分类号： G06K9/62 , A61B6/502 , A61B6/5217 , G06K2209/053 , G06T7/136 , G06T7/40 , G06T7/60 , G06T2207/10072 , G06T2207/10116 , G06T2207/20064 , G06T2207/30068
摘要： Systems, computer-readable media, methods, and a medical imaging system are presented that compute and output a density estimate of a breast. The density estimate may be computed using information from at least two digital images, wherein each image represents a view of at least a portion of the breast from a different specific angle. The density estimate may be computed using information from at least one digital breast image and at least one digital opposite breast image, wherein the at least one digital breast image represents a view of at least a portion of the breast from a specific angle and wherein the at least one digital opposite breast image represents a view of at least a portion of the opposite breast. The density estimate may be computed using computed parenchyma information, the parenchyma information being computed using texture information and density information derived from at least one digital image of at least a portion of the breast. The density estimate may be computed using computed parenchyma information, the parenchyma information being computed from at least one digital image using computed vessel line information, the computed vessel line information being computed from the at least one digital image.
摘要翻译：提出了计算和输出乳房密度估计的系统，计算机可读介质，方法和医学成像系统。可以使用来自至少两个数字图像的信息来计算密度估计，其中每个图像表示来自不同特定角度的乳房的至少一部分的视图。可以使用来自至少一个数字乳房图像和至少一个数字相对乳房图像的信息来计算密度估计，其中所述至少一个数字乳房图像从特定角度表示乳房的至少一部分的视图，并且其中至少一个数字相对乳房图像表示相对乳房的至少一部分的视图。可以使用计算的实质信息计算密度估计，使用纹理信息计算的薄壁信息和从至少一部分乳房的至少一个数字图像导出的密度信息。可以使用计算的实质信息来计算密度估计，使用计算的血管线信息从至少一个数字图像计算实体信息，所计算的血管线信息是从至少一个数字图像计算的。

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