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1.发明申请METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR AN INTELLIGENT SEARCH WORKSTATION FOR COMPUTER ASSISTED INTERPRETATION OF MEDICAL IMAGES 审中-公开智能搜索工具台的计算机辅助解读医学图像的方法，系统和计算机可读介质
公开(公告)号：WO0157777A3
公开(公告)日：2002-08-22
申请号：PCT/US0100680
申请日：2001-02-05
Applicant: ARCH DEV CORP
Inventor： GIGER MARYELLEN L , VYBORNY CARL J , HUO ZHIMIN , LAN LI
CPC classification number: G06F19/321 , A61B6/502 , G06T7/0012 , G06T2207/30068 , G16H50/20 , G16H50/70
Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists. Techniques include novel developments and implementations of computer-extracted features for similarity calculation and novel methods for the display of the unknown case amongst known cases with and without the computer-determined diagnoses.
Abstract translation: 一种用于智能搜索显示器的方法，系统和计算机可读介质，已经并入了自动计算机图像分析。 在观察未知的乳房X线照相术的情况下，显示器显示计算机分类输出以及具有已知诊断（例如恶性与良性）的病变的图像以及类似的计算机提取的特征。 搜索中使用的相似性索引可以由放射科医师选择，以基于单个特征，多个特征或计算机估计恶性肿瘤的可能性。 具体地说，系统包括计算已知数据库中的图像的特征，计算未知情况的特征，计算相似性指数，沿着未知情况存在的概率分布曲线显示已知情况。 技术包括用于相似性计算的计算机提取特征的新颖开发和实现，以及用于在具有和不具有计算机确定的诊断的情况下在已知病例中显示未知病例的新颖方法。
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2.发明申请
公开(公告)号：WO9963480A8
公开(公告)日：2000-02-17
申请号：PCT/US9911794
申请日：1999-06-04
Applicant: ARCH DEV CORP
Inventor： HUO ZHIMIN , GIGER MARYELLEN L
CPC classification number: G06T7/0012 , G06K9/6277
Abstract: A method, system and computer readable medium for the computerized assessment of breast cancer risk, wherein a digital image (1100) of a breast is obtained and at least one feature area extracted (1102) from a region of interest in the digital. The extracted features (1102) are compared with a predetermined model (1106) associating patterns of the extracted features with a risk estimate (1108). Preferred features to be extracted from the digital image include: 1) one or more features based on absolute values of gray levels of pixels in said region of interest; 2) one or more features based on gray-level histogram analysis of pixels in said region of interest; (3) one or more features based on Fourier analysis of pixels values in said region of interest; 4) one or more features based on a spatial relationship among gray levels of pixels within the region of interest.
Abstract translation: 一种用于乳腺癌风险的计算机化评估的方法，系统和计算机可读介质，其中获得乳房的数字图像（1100）和从所述数字图像中的感兴趣区域提取（1102）的至少一个特征区域。 将提取的特征（1102）与将提取的特征的模式与风险估计（1108）相关联的预定模型（1106）进行比较。 要从数字图像提取的优选特征包括：1）基于所述感兴趣区域中的像素的灰度级的绝对值的一个或多个特征; 2）基于所述感兴趣区域中的像素的灰度直方图分析的一个或多个特征; （3）基于所述感兴趣区域中的像素值的傅里叶分析的一个或多个特征; 4）基于感兴趣区域内的像素的灰度级之间的空间关系的一个或多个特征。
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3.发明公开AUTOMATED METHOD AND SYSTEM FOR IMPROVED COMPUTERIZED DETECTION AND CLASSIFICATION OF MASSES IN MAMMOGRAMS 失效FOR乳房X线照片改进计算机化的检测和分类大众的自动方法和系统
公开(公告)号：EP0731952A4
公开(公告)日：1997-01-22
申请号：EP95903554
申请日：1994-11-29
Applicant: ARCH DEV CORP
Inventor： GIGER MARYELLEN L , DOI KUNIO , LU PING , HUO ZHIMIN
CPC classification number: G06K9/00127 , G06T7/0012
Abstract: A method and system for automated detection and classification of masses in mammograms. This method and system include the performance of iterative, multi-level gray level thresholding (202), followed by lesion extraction (203) and feature extraction techniques (205) for classifying true masses from false-postive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses including multi-gray-level thresholding (202) of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e. either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood malignancy.
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4.发明公开METHOD AND SYSTEM FOR THE COMPUTERIZED ASSESSMENT OF BREAST CANCER RISK 审中-公开方法和系统对乳腺癌的计算机辅助FINDING
公开(公告)号：EP1082695A4
公开(公告)日：2003-02-12
申请号：EP99955351
申请日：1999-06-04
Applicant: ARCH DEV CORP
Inventor： HUO ZHIMIN , GIGER MARYELLEN L
CPC classification number: G06T7/0012 , G06K9/6277
Abstract: A method, system and computer readable medium for the computerized assessment of breast cancer risk, wherein a digital image (1100) of a breast is obtained and at least one feature area extracted (1102) from a region of interest in the digital. The extracted features (1102) are compared with a predetermined model (1106) associating patterns of the extracted features with a risk estimate (1108). Preferred features to be extracted from the digital image include: 1) one or more features based on absolute values of gray levels of pixels in said region of interest; 2) one or more features based on gray-level histogram analysis of pixels in said region of interest; (3) one or more features based on Fourier analysis of pixels values in said region of interest; 4) one or more features based on a spatial relationship among gray levels of pixels within the region of interest.
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公开(公告)号：AT239273T
公开(公告)日：2003-05-15
申请号：AT95903554
申请日：1994-11-29
Applicant: ARCH DEV CORP
Inventor： GIGER MARYELLEN L , DOI KUNIO , LU PING , HUO ZHIMIN
IPC: A61B6/00 , G06K9/00 , G06T1/00 , G06T7/00 , G06T5/00 , G06K9/46 , G06K9/44 , G06K9/62 , G06K9/38
Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.
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Patent Agency Ranking
公开(公告)号：AU1257095A
公开(公告)日：1995-06-13
申请号：AU1257095
申请日：1994-11-29
Applicant: ARCH DEV CORP
Inventor： GIGER MARYELLEN L , DOI KUNIO , LU PING , HUO ZHIMIN
Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.
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公开(公告)号：DE69432601D1
公开(公告)日：2003-06-05
申请号：DE69432601
申请日：1994-11-29
Applicant: ARCH DEV CORP
Inventor： GIGER L , DOI KUNIO , LU PING , HUO ZHIMIN
IPC: A61B6/00 , G06K9/00 , G06T1/00 , G06T7/00 , G06T5/00 , G06K9/46 , G06K9/44 , G06K9/62 , G06K9/38
Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.
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公开(公告)号：AU4317799A
公开(公告)日：1999-12-20
申请号：AU4317799
申请日：1999-06-04
Applicant: ARCH DEV CORP
Inventor： HUO ZHIMIN , GIGER MARYELLEN L
Abstract: A method, system and computer readable medium for the computerized assessment of breast cancer risk, wherein a digital image of a breast is obtained and at least one feature, and typically plural features, are extracted from a region of interest in the digital. The extracted features are compared with a predetermined model associating patterns of the extracted features with a risk estimate derived from corresponding feature patterns associated with a predetermined model based on gene carrier information or clinical information, or both gene carrier information and clinical information, and a risk classification index is output as a result of the comparison. Preferred features to be extracted from the digital image include 1) one or more features based on absolute values of gray levels of pixels in said region of interest, 2) one or more features based on gray-level histogram analysis of pixels in said region of interest; 3) one or more features based on Fourier analysis of pixel values in said region of interest; and 4) one or more features based on a spatial relationship among gray levels of pixels within the region of interest.
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公开(公告)号：CA2334227A1
公开(公告)日：1999-12-09
申请号：CA2334227
申请日：1999-06-04
Applicant: ARCH DEV CORP
Inventor： HUO ZHIMIN , GIGER MARYELLEN L
Abstract: A method, system and computer readable medium for the computerized assessmen t of breast cancer risk, wherein a digital image (1100) of a breast is obtaine d and at least one feature area extracted (1102) from a region of interest in the digital. The extracted features (1102) are compared with a predetermined model (1106) associating patterns of the extracted features with a risk estimate (1108). Preferred features to be extracted from the digital image include: 1) one or more features based on absolute values of gray levels of pixels in said region of interest; 2) one or more features based on gray-lev el histogram analysis of pixels in said region of interest; (3) one or more features based on Fourier analysis of pixels values in said region of interest; 4) one or more features based on a spatial relationship among gray levels of pixels within the region of interest.
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10.发明专利
公开(公告)号：AU687958B2
公开(公告)日：1998-03-05
申请号：AU1257095
申请日：1994-11-29
Applicant: ARCH DEV CORP
Inventor： GIGER MARYELLEN L , DOI KUNIO , LU PING , HUO ZHIMIN
Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.
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