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公开(公告)号：US20080118124A1
公开(公告)日：2008-05-22
申请号：US11907967
申请日：2007-10-18
申请人： Anant Madabhushi , Michael Feldman , John Tomaszewski , Mark Rosen , Jianbo Shi
发明人： Anant Madabhushi , Michael Feldman , John Tomaszewski , Mark Rosen , Jianbo Shi
CPC分类号： G06K9/6224 , G06K9/469 , G06K2209/05 , G06T7/0012 , G06T2207/10088 , G06T2207/30004 , G06T2207/30096
摘要： This invention relates to supervised or unsupervised classification of biological datasets. Specifically, the invention relates to the use of Graph Embedding as a method of reducing dimensionality thereby improving supervised classification of classes, both conventional and new ones.
摘要翻译： 本发明涉及生物数据集的监督或无监督分类。 具体地，本发明涉及使用图形嵌入作为降低维度的方法，从而改进传统和新的类的监督分类。
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公开(公告)号：US08204315B2
公开(公告)日：2012-06-19
申请号：US11907967
申请日：2007-10-18
CPC分类号： G06K9/6224 , G06K9/469 , G06K2209/05 , G06T7/0012 , G06T2207/10088 , G06T2207/30004 , G06T2207/30096
摘要： This invention relates to supervised or unsupervised classification of biological datasets. Specifically, the invention relates to the use of Graph Embedding as a method of reducing dimensionality thereby improving supervised classification of classes, both conventional and new ones.
摘要翻译： 本发明涉及生物数据集的监督或无监督分类。 具体地，本发明涉及使用图形嵌入作为降低维度的方法，从而改进传统和新的类的监督分类。
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3.发明申请SYSTEM AND METHOD FOR ACCURATE AND RAPID IDENTIFICATION OF DISEASED REGIONS ON BIOLOGICAL IMAGES WITH APPLICATIONS TO DISEASE DIAGNOSIS AND PROGNOSIS 有权用于精神和快速识别生物图像疾病区域的系统和方法，应用于疾病诊断和预防公开(公告)号：US20110243417A1
公开(公告)日：2011-10-06
申请号：US13061843
申请日：2009-09-03
IPC分类号： G06K9/00
CPC分类号： G06K9/4638 , G06K9/0014 , G06K9/3233 , G06K2209/053 , G06T7/0012 , G06T7/46 , G06T2207/10056 , G06T2207/30024 , G06T2207/30081
摘要： The present invention relates to a method and system for detecting biologically relevant structures in a hierarchical fashion, beginning at a low-resolution and proceeding to higher levels of resolution. The present invention also provides probabilistic pairwise Markov models (PPMMs) to classify these relevant structures. The invention is directed to a novel classification approach which weighs the importance of these structures. The present invention also provides a fast, efficient computer-aided detection/diagnosis (CAD) system capable of rapidly processing medical images (i.e. high throughput). The computer-aided detection/diagnosis (CAD) system of the present invention allows for rapid analysis of medical images the improving the ability to effectively detect, diagnose, and treat certain diseases.
摘要翻译： 本发明涉及以低分辨率开始并以更高级别的分辨率从分级方式检测生物相关结构的方法和系统。 本发明还提供用于对这些相关结构进行分类的概率成对的马尔可夫模型（PPMM）。 本发明涉及一种重视这些结构的重要性的新型分类方法。 本发明还提供了能够快速处理医学图像（即，高吞吐量）的快速，有效的计算机辅助检测/诊断（CAD）系统。 本发明的计算机辅助检测/诊断（CAD）系统允许快速分析医学图像，提高有效检测，诊断和治疗某些疾病的能力。
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4.发明授权System and method for accurate and rapid identification of diseased regions on biological images with applications to disease diagnosis and prognosis 有权用于准确，快速地鉴别生物图像上疾病区域的系统和方法，应用于疾病诊断和预后公开(公告)号：US08718340B2
公开(公告)日：2014-05-06
申请号：US13061843
申请日：2009-09-03
CPC分类号： G06K9/4638 , G06K9/0014 , G06K9/3233 , G06K2209/053 , G06T7/0012 , G06T7/46 , G06T2207/10056 , G06T2207/30024 , G06T2207/30081
摘要： The present invention relates to a method and system for detecting biologically relevant structures in a hierarchical fashion, beginning at a low-resolution and proceeding to higher levels of resolution. The present invention also provides probabilistic pairwise Markov models (PPMMs) to classify these relevant structures. The invention is directed to a novel classification approach which weighs the importance of these structures. The present invention also provides a fast, efficient computer-aided detection/diagnosis (CAD) system capable of rapidly processing medical images (i.e. high throughput). The computer-aided detection/diagnosis (CAD) system of the present invention allows for rapid analysis of medical images the improving the ability to effectively detect, diagnose, and treat certain diseases.
摘要翻译： 本发明涉及以低分辨率开始并以更高级别的分辨率从分级方式检测生物相关结构的方法和系统。 本发明还提供用于对这些相关结构进行分类的概率成对的马尔可夫模型（PPMM）。 本发明涉及一种重视这些结构的重要性的新型分类方法。 本发明还提供了能够快速处理医学图像（即，高吞吐量）的快速，有效的计算机辅助检测/诊断（CAD）系统。 本发明的计算机辅助检测/诊断（CAD）系统允许快速分析医学图像，提高有效检测，诊断和治疗某些疾病的能力。
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5.发明授权Computer assisted diagnosis (CAD) of cancer using multi-functional, multi-modal in-vivo magnetic resonance spectroscopy (MRS) and imaging (MRI) 有权使用多功能，多模式体内磁共振波谱（MRS）和成像（MRI）的癌症的计算机辅助诊断（CAD）公开(公告)号：US08295575B2
公开(公告)日：2012-10-23
申请号：US12740383
申请日：2008-10-29
申请人： Michael D. Feldman , Satish Viswanath , Pallavi Tiwari , Robert Toth , Anant Madabhushi , John Tomaszeweski , Mark Rosen
发明人： Michael D. Feldman , Satish Viswanath , Pallavi Tiwari , Robert Toth , Anant Madabhushi , John Tomaszeweski , Mark Rosen
CPC分类号： G06T7/0012 , G06K9/4619 , G06K9/6252 , G06K2209/051 , G06T2207/30004 , G06T2207/30081
摘要： This invention relates to computer-assisted diagnostics and classification of prostate cancer. Specifically, the invention relates to segmentation of the prostate boundary on MRI images, cancer detection using multimodal multi-protocol MR data; and their integration for a computer-aided diagnosis and classification system for prostate cancer.
摘要翻译： 本发明涉及前列腺癌的计算机辅助诊断和分类。 具体地，本发明涉及MRI图像上的前列腺边界的分割，使用多模式多协议MR数据的癌症检测; 以及它们与前列腺癌计算机辅助诊断和分类系统的整合。
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6.发明申请COMPUTER ASSISTED DIAGNOSIS (CAD) OF CANCER USING MULTI-FUNCTIONAL, MULTI-MODAL IN-VIVO MAGNETIC RESONANCE SPECTROSCOPY (MRS) AND IMAGING (MRI) 有权使用多功能，多模式血管内共振光谱（MRS）和成像（MRI）的计算机辅助诊断（CAD）公开(公告)号：US20100329529A1
公开(公告)日：2010-12-30
申请号：US12740383
申请日：2008-10-29
申请人： Michael D Feldman , Satish Viswanath , Pallavi Tiwari , Robert Toth , Anant Madabhushi , John Tomaszeweski , Mark Rosen
发明人： Michael D Feldman , Satish Viswanath , Pallavi Tiwari , Robert Toth , Anant Madabhushi , John Tomaszeweski , Mark Rosen
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/4619 , G06K9/6252 , G06K2209/051 , G06T2207/30004 , G06T2207/30081
摘要： This invention relates to computer-assisted diagnostics and classification of prostate cancer. Specifically, the invention relates to segmentation of the prostate boundary on MRI images, cancer detection using multimodal multi-protocol MR data; and their integration for a computer-aided diagnosis and classification system for prostate cancer.
摘要翻译： 本发明涉及前列腺癌的计算机辅助诊断和分类。 具体地，本发明涉及MRI图像上的前列腺边界的分割，使用多模式多协议MR数据的癌症检测; 并将其与前列腺癌计算机辅助诊断和分类系统相结合。
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7.发明申请公开(公告)号：US20110026798A1
公开(公告)日：2011-02-03
申请号：US12867349
申请日：2009-02-19
申请人： Anant Madabhushi , Shannon Agner , Mark Rosen
发明人： Anant Madabhushi , Shannon Agner , Mark Rosen
IPC分类号： G06K9/00
CPC分类号： G01R33/56 , G01R33/5608 , G01R33/56308 , G06T7/0012 , G06T7/11 , G06T2207/10096 , G06T2207/20076 , G06T2207/20108 , G06T2207/20116 , G06T2207/30068 , G06T2207/30096
摘要： A method and apparatus for classifying possibly malignant lesions from sets of DCE-MRI images includes receiving a set of MRI slice images obtained at respectively different times, where each slice image includes voxels representative of at least one region of interest (ROI). The images are processed to determine the boundaries of the ROIs and the voxels within the identified boundaries in corresponding regions of the images from each time period are processed to extract kinetic texture features. The kinetic texture features are then used in a classification process which classifies the ROIs as malignant or benign. The malignant lesions are further classified to separate TN lesions from non-TN lesions.
摘要翻译： 从DCE-MRI图像集合分类可能的恶性病变的方法和装置包括接收在不同时间获得的一组MRI切片图像，其中每个切片图像包括代表至少一个感兴趣区域（ROI）的体素。 处理图像以确定ROI的边界，并且处理来自每个时间段的图像的相应区域中所识别的边界内的体素以提取动力学纹理特征。 然后将动力学纹理特征用于将ROI分类为恶性或良性的分类过程。 恶性病变进一步分类为TN病变与非TN病变。
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8.发明授权公开(公告)号：US08774479B2
公开(公告)日：2014-07-08
申请号：US12867349
申请日：2009-02-19
申请人： Anant Madabhushi , Shannon Agner , Mark Rosen
发明人： Anant Madabhushi , Shannon Agner , Mark Rosen
IPC分类号： G06K9/00
CPC分类号： G01R33/56 , G01R33/5608 , G01R33/56308 , G06T7/0012 , G06T7/11 , G06T2207/10096 , G06T2207/20076 , G06T2207/20108 , G06T2207/20116 , G06T2207/30068 , G06T2207/30096
摘要： A method and apparatus for classifying possibly malignant lesions from sets of DCE-MRI images includes receiving a set of MRI slice images obtained at respectively different times, where each slice image includes voxels representative of at least one region of interest (ROI). The images are processed to determine the boundaries of the ROIs and the voxels within the identified boundaries in corresponding regions of the images from each time period are processed to extract kinetic texture features. The kinetic texture features are then used in a classification process which classifies the ROIs as malignant or benign. The malignant lesions are further classified to separate TN lesions from non-TN lesions.
摘要翻译： 从DCE-MRI图像集合分类可能的恶性病变的方法和装置包括接收在不同时间获得的一组MRI切片图像，其中每个切片图像包括代表至少一个感兴趣区域（ROI）的体素。 处理图像以确定ROI的边界，并且处理来自每个时间段的图像的相应区域中所识别的边界内的体素以提取动力学纹理特征。 然后将动力学纹理特征用于将ROI分类为恶性或良性的分类过程。 恶性病变进一步分类为TN病变与非TN病变。
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9.发明申请DEFINING QUANTITATIVE SIGNATURES FOR DIFFERENT GLEASON GRADES OF PROSTATE CANCER USING MAGNETIC RESONANCE SPECTROSCOPY 审中-公开使用磁共振光谱法确定前列腺癌不同年龄组的定量检测标志公开(公告)号：US20100169024A1
公开(公告)日：2010-07-01
申请号：US12555556
申请日：2009-09-08
申请人： Anant Madabhushi , Pallavi Tiwari , Mark Rosen
发明人： Anant Madabhushi , Pallavi Tiwari , Mark Rosen
IPC分类号： G06F19/00
CPC分类号： G06T7/0012 , G06T7/42 , G06T2207/10088 , G06T2207/10096 , G06T2207/30004 , G06T2207/30081
摘要： A method for classifying a possible cancer from a magnetic resonance spectrographic (MRS) dataset includes extracting at least one feature from the MRS dataset as being identified with the possible cancer and embedding the extracted feature into a low dimensional space to form an embedded space. The method then clusters the embedded space into clusters representing a plurality of predetermined classes and spectrally decomposing the clusters to identify substantially significant independent metabolic signatures. The method then classifies the possible cancer as belong to one of at least two cancer classes based on the identified independent metabolic signatures.
摘要翻译： 用于从磁共振谱（MRS）数据集分类可能的癌症的方法包括从MRS数据集中提取至少一个特征，并将其识别为可能的癌症，并将所提取的特征嵌入到低维空间中以形成嵌入的空间。 然后，该方法将嵌入空间聚类成代表多个预定类别的簇，并且频谱分解簇以识别基本上显着的独立代谢特征。 然后该方法基于所鉴定的独立代谢特征将可能的癌症分类为至少两种癌症类别之一。
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公开(公告)号：US08442285B2
公开(公告)日：2013-05-14
申请号：US12593651
申请日：2008-04-01
IPC分类号： G06K9/00
CPC分类号： G06K9/6231 , G06T7/32 , G06T7/33 , G06T7/35 , G06T2207/10088 , G06T2207/30004 , G06T2207/30081
摘要： This invention relates to medical image registration. Specifically, the invention relates to a combined feature ensemble mutual information (COFEMI) for robust inter-modal, inter-protocol image registration.
摘要翻译： 本发明涉及医学图像配准。 具体地说，本发明涉及用于鲁棒模式间协议间映像配准的组合特征集合互信息（COFEMI）。
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