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11. 发明申请

WO2003046808A2 COMPUTERIZED SCHEME FOR DISTINGUISHING BETWEEN BENIGN AND MALIGNANT NODULES IN THORACIC COMPUTED TOMOGRAPHY SCANS BY USE OF SIMILAR IMAGES 审中-公开
标题翻译：用于通过使用类似图像在胸部计算机断层扫描仪中判断阴性和恶性肿瘤之间的计算机方案
公开(公告)号：WO2003046808A2
公开(公告)日：2003-06-05
申请号：PCT/US2002/033654
申请日：2002-11-22
申请人： UNIVERSITY OF CHICAGO , LI, Qiang , DOI, Kunio
发明人： LI, Qiang , DOI, Kunio
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T2207/30061
摘要： A computerized scheme to assist radiologists in improving the diagnostic accuracy for abnormalities (e.g., nodules) in medical images by use of similar images for malignant abnormalities and benign abnormalities. The method includes developing a database of medical images which includes both confirmed cancers and confirmed benign abnormalities; obtaining a medical image including at least one abnormality; selecting at least one feature for comparison from an unknown abnormality and at least one known abnormality, respectively; determining a similarity measure between an unknown, undiagnosed abnormality and at least one of the previously diagnosed abnormalities; and selecting from the database of known abnormalities at least one known abnormality for comparison with the unknown abnormality in order to determine a likelihood of malignancy. In one embodiment, an artificial neural network is employed to determine a similarity measure between an unknown nodule and at least one known nodule.
摘要翻译：一种计算机辅助放射科医生通过使用类似图像进行恶性异常和良性异常改善医学图像异常（例如结节）的诊断准确性的方法。该方法包括开发包括确认的癌症和确诊的良性异常的医学图像数据库; 获得包括至少一个异常的医学图像; 从未知异常和至少一个已知异常分别选择至少一个用于比较的特征; 确定未知的，未诊断的异常与先前诊断的异常中的至少一个之间的相似性度量; 从数据库中选择已知异常至少一个已知异常，以便与未知异常进行比较，以便确定恶性肿瘤的可能性。在一个实施例中，使用人造神经网络来确定未知结节与至少一个已知结节之间的相似性度量。

12. 发明申请

WO0028466A9 SYSTEM FOR COMPUTERIZED PROCESSING OF CHEST RADIOGRAPHIC IMAGES 审中-公开
标题翻译：用于计算机图像处理的系统
公开(公告)号：WO0028466A9
公开(公告)日：2000-09-28
申请号：PCT/US9924007
申请日：1999-11-05
申请人： ARCH DEV CORP , DOI KUNIO , LI QIANG , KATSURAGAWA SHIGEHIKO , ISHIDA TAKAYUKI
发明人： DOI KUNIO , LI QIANG , KATSURAGAWA SHIGEHIKO , ISHIDA TAKAYUKI
IPC分类号： A61B6/00 , G06T1/00 , G06T3/00 , G06T5/50 , G06T7/60 , G06K9/00
CPC分类号： G06T3/0068 , G06T5/50 , G06T7/174
摘要： A method, system and computer readable medium for computerized processing of chest images including obtaining a digital first image of a chest (S100); producing a second image which is a mirror image (S300) of the first image; performing image warping on one of the first and second images to produce a warped image (S400) which is registered to the other of the first and second images; and subtracting the warped image from the other image to generate a subtraction image (S600). Another embodiment includes obtaining a digital first image of the chest of a subject; detecting ribcage edges on both sides of the lungs in the first chest image; determining average horizontal locations of the left and right ribcage edges at plural vertical locations; fitting the determined average horizontal locations to a straight line to derive a midline; rotating the chest image so that the midline is vertical; and shifting the rotated image to produce a lateral inclination corrected (S200) second image with the midline centered in the lateral inclination corrected image.
摘要翻译：一种用于计算机化处理胸部图像的方法，系统和计算机可读介质，包括获得胸部的数字第一图像（S100）; 产生作为第一图像的镜像（S300）的第二图像; 在第一和第二图像之一上执行图像扭曲以产生被注册到第一和第二图像中的另一个的翘曲图像（S400）; 并从另一图像中减去翘曲图像以产生减法图像（S600）。另一实施例包括获得对象胸部的数字第一图像; 在第一胸部图像中检测肺两侧的肋骨边缘; 确定在多个垂直位置处的左和右胸腔边缘的平均水平位置; 将确定的平均水平位置拟合到直线以导出中线; 旋转胸部图像，使中线垂直; 并移动旋转的图像以产生横向倾斜校正（S200）第二图像，其中心线位于横向倾斜校正图像中。

13. 发明申请

WO2007062135A3 COMPUTER-AIDED METHOD FOR DETECTION OF INTERVAL CHANGES IN SUCCESSIVE WHOLE-BODY BONE SCANS AND RELATED COMPUTER PROGRAM PRODUCT AND SYSTEM 审中-公开
公开(公告)号：WO2007062135A3
公开(公告)日：2007-05-31
申请号：PCT/US2006/045230
申请日：2006-11-22
申请人： SHIRAISHI, Junji , DOI, Kunio , APPELBAUM, Daniel , LI, Qiang , PU, Yonglin
发明人： SHIRAISHI, Junji , DOI, Kunio , APPELBAUM, Daniel , LI, Qiang , PU, Yonglin
IPC分类号： G06K9/00
摘要： A method of producing an image to aid detection of a change in progress of a disease in a patient is described. In the method, a first image of a distribution of a radioisotope in the patient is obtained. A second image of the distribution of the radioisotope in the patient is also obtained. At least one of the first and second images are then normalized (1 :140). One of the images is warped to match the other image using a multiple-segment matching method (1 : 160). The first image is subtracted from the second image to form a subtraction image (1 : 220). Finally, the resulting subtraction image is displayed.

14. 发明申请

WO2007062135A2 COMPUTER-AIDED METHOD FOR DETECTION OF INTERVAL CHANGES IN SUCCESSIVE WHOLE-BODY BONE SCANS AND RELATED COMPUTER PROGRAM PRODUCT AND SYSTEM 审中-公开
标题翻译：用于检测后续全身骨扫描和相关计算机程序产品和系统的间隔变化的计算机辅助方法
公开(公告)号：WO2007062135A2
公开(公告)日：2007-05-31
申请号：PCT/US2006045230
申请日：2006-11-22
申请人： SHIRAISHI JUNJI , DOI KUNIO , APPELBAUM DANIEL , LI QIANG , PU YONGLIN
发明人： SHIRAISHI JUNJI , DOI KUNIO , APPELBAUM DANIEL , LI QIANG , PU YONGLIN
IPC分类号： G06T7/20
CPC分类号： G06T7/0012 , G06T5/50 , G06T7/32 , G06T2207/30008
摘要： A method of producing an image to aid detection of a change in progress of a disease in a patient is described. In the method, a first image of a distribution of a radioisotope in the patient is obtained. A second image of the distribution of the radioisotope in the patient is also obtained. At least one of the first and second images are then normalized. One of the images is warped to match the other image using a multiple-segment matching method. The first image is subtracted from the second image to form a subtraction image. Finally, the resulting subtraction image is displayed.
摘要翻译：描述了一种产生图像以帮助检测患者疾病进展变化的方法。在该方法中，获得患者中放射性同位素分布的第一图像。还获得了患者中放射性同位素分布的第二图像。然后对第一和第二图像中的至少一个进行标准化。其中一个图像被扭曲以使用多段匹配方法匹配其他图像。从第二图像中减去第一图像以形成减影图像。最后，显示所得到的减法图像。

15. 发明申请

WO2003087983A2 MASSIVE TRAINING ARTIFICIAL NEURAL NETWORK (MTANN) FOR DETECTING ABNORMALITIES IN MEDICAL IMAGES 审中-公开
标题翻译：用于检测医学图像异常的大规模训练人工神经网络（MTANN）
公开(公告)号：WO2003087983A2
公开(公告)日：2003-10-23
申请号：PCT/US2003/010468
申请日：2003-04-14
申请人： THE UNIVERSITY OF CHICAGO , SUZUKI, Kenji , DOI, Kunio
发明人： SUZUKI, Kenji , DOI, Kunio
IPC分类号： G06F
CPC分类号： G06T7/0012
摘要： A method of training an artificial neural network (ANN) involves receiving a likelihood distribution map as a teacher image, receiving a training image, moving a local window across sub-regions of the training image to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to the ANN so that it provides output pixel values that are compared to output pixel values of corresponding teacher image pixel values to determine an error, and training the ANN to reduce the error. A method of detecting a target structure in an image involves scanning a local window across sub-regions of the image by moving the local window for each sub-region so as to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to an ANN so that it provides respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map. Another method for detecting a target structure involves training N parallel ANNs on either (A) a same target structure and N mutually different non-target structures, or (B) a same non-target structure and N mutually different target structures, the ANNs outputting N respective indications of whether the image includes a target structure or a non-target structure, and combining the N indications to form a combined indication of whether the image includes a target structure or a non-target structure. The invention provides related apparatus and computer program products storing executable instructions to perform the methods.
摘要翻译：训练人造神经网络（ANN）的方法包括：接收似然分布图作为教师图像，接收训练图像，在训练图像的子区域上移动局部窗口以获得各自的子区域像素组，输入子区域像素集合到ANN，使得它提供与相应的教师图像像素值的输出像素值进行比较的输出像素值，以确定错误，并训练ANN以减少误差。检测图像中的目标结构的方法涉及通过移动每个子区域的局部窗口扫描图像的子区域中的局部窗口，以便获得各个子区域像素组，输入子区域像素组到ANN，使得其提供表示各个图像像素是目标结构的一部分的可能性的各个输出像素值，输出像素值共同构成似然分布图。用于检测目标结构的另一种方法涉及在（A）相同目标结构和N个相互不同的非目标结构上训练N个并行ANN，或者（B）相同的非目标结构和N个相互不同的目标结构，ANN输出 N分别表示图像是否包括目标结构或非目标结构，并且组合N个指示以形成图像是否包括目标结构或非目标结构的组合指示。本发明提供了存储执行方法的可执行指令的相关装置和计算机程序产品。

16. 发明申请

WO03046808A3 METHOD FOR DISTINGUISHING BENIGN AND MALIGNANT NODULES 审中-公开
标题翻译：染色剂和恶臭剂的方法
公开(公告)号：WO03046808A3
公开(公告)日：2003-09-12
申请号：PCT/US0233654
申请日：2002-11-22
申请人： UNIV CHICAGO , LI QIANG , DOI KUNIO
发明人： LI QIANG , DOI KUNIO
IPC分类号： G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , G06T2207/30061
摘要： A computerized scheme to assist radiologists in improving the diagnostic accuracy for abnormalities (e.g., nodules) in medical images by use of similar images for malignant abnormalities and benign abnormalities. The method includes developing a database of medical images which includes both confirmed cancers and confirmed benign abnormalities; obtaining a medical image including at least one abnormality; selecting at least one feature for comparison from an unknown abnormality and at least one known abnormality, respectively; determining a similarity measure between an unknown, undiagnosed abnormality and at least one of the previously diagnosed abnormalities; and selecting from the database of known abnormalities at least one known abnormality for comparison with the unknown abnormality in order to determine a likelihood of malignancy. In one embodiment, an artificial neural network is employed to determine a similarity measure between an unknown nodule and at least one known nodule.
摘要翻译：一种计算机辅助放射科医生通过使用类似图像进行恶性异常和良性异常改善医学图像异常（例如结节）的诊断准确性的方法。该方法包括开发包括确认的癌症和确诊的良性异常的医学图像数据库; 获得包括至少一个异常的医学图像; 从未知异常和至少一个已知异常分别选择至少一个用于比较的特征; 确定未知的，未诊断的异常与先前诊断的异常中的至少一个之间的相似性度量; 从数据库中选择已知异常至少一个已知异常，以便与未知异常进行比较，以便确定恶性肿瘤的可能性。在一个实施例中，使用人造神经网络来确定未知结节与至少一个已知结节之间的相似性度量。

17. 发明申请

WO2003046831A3 A NOVEL SUBTRACTION TECHNIQUE FOR COMPUTERIZED DETECTION OF SMALL LUNG NODULES IN COMPUTER TOMOGRAPHY IMAGES 审中-公开
公开(公告)号：WO2003046831A3
公开(公告)日：2003-06-05
申请号：PCT/US2002/033651
申请日：2002-11-22
申请人： UNIVERSITY OF CHICAGO , DOI, Kunio , ISHIDA, Takayuki , KATSURAGAWA, Shigehiko
发明人： DOI, Kunio , ISHIDA, Takayuki , KATSURAGAWA, Shigehiko
IPC分类号： G06K9/00
摘要： A method, system, computer readable medium (36) and apparatus for computerized detection of lung nodules in computer tomography (10) images, by which mask images are created such that subtractions of the mask image from a targeted CT section image reveal or highlight small lung nodules in the target CT section (118). The mask image is created utilizing the targeted CT section image along with other CT section images generated from the same CT scan (130).

18. 发明申请

WO2003046831A2 A NOVEL SUBTRACTION TECHNIQUE FOR COMPUTERIZED DETECTION OF SMALL LUNG NODULES IN COMPUTER TOMOGRAPHY IMAGES 审中-公开
标题翻译：一种用于计算机图像中小型肺细胞计算机检测的新技术
公开(公告)号：WO2003046831A2
公开(公告)日：2003-06-05
申请号：PCT/US2002/033651
申请日：2002-11-22
申请人： UNIVERSITY OF CHICAGO , DOI, Kunio , ISHIDA, Takayuki , KATSURAGAWA, Shigehiko
发明人： DOI, Kunio , ISHIDA, Takayuki , KATSURAGAWA, Shigehiko
IPC分类号： G06T
CPC分类号： G06T5/30 , G06T5/50 , G06T7/11 , G06T2207/10081 , G06T2207/20224 , G06T2207/30064
摘要： A method, system, computer readable medium and apparatus for computerized detection of lung nodules in computer tomography images, by which mask images are created such that subtractions of the mask image from a targeted CT section image reveal or highlight small lung nodules in the target CT section. The mask image is created utilizing the targeted CT section image along with other CT section images generated from the same CT scan. Based on these other section CT images and the targeted CT section image, a mask image can be created that is very similar to the target CT section image, but without the presence of small lung nodules. When the mask image is subtracted from the targeted CT section image, the differences between the mask images and the CT section images reveal small lung nodules. The mask image may be created by linear interpolation or a morphological filtered image.
摘要翻译：一种用于计算机化检测计算机断层摄影图像中的肺结节的方法，系统，计算机可读介质和装置，通过该方法创建掩模图像，使得来自目标CT部分图像的掩模图像的减法显示或突出显示目标CT中的小肺结节部分。使用目标CT部分图像以及从相同CT扫描生成的其他CT部分图像创建掩模图像。基于这些其他部分CT图像和目标CT部分图像，可以创建与目标CT部分图像非常相似但不存在小肺结节的掩模图像。当从目标CT部分图像中减去掩模图像时，掩模图像和CT部分图像之间的差异显示出小的肺结节。掩模图像可以通过线性内插或形态滤波图像来创建。

19. 发明申请

WO2003046809A1 COMPUTERIZED METHOD FOR DETERMINATION OF THE LIKELIHOOD OF MALIGNANCY FOR PULMONARY NODULES ON LOW-DOSE CT 审中-公开
标题翻译：计算方法用于确定低剂量CT下肺动脉狭窄的恶性肿瘤
公开(公告)号：WO2003046809A1
公开(公告)日：2003-06-05
申请号：PCT/US2002/036121
申请日：2002-11-22
申请人： UNIVERSITY OF CHICAGO , DOI, Kunio , AOYAMA, Masahito , LI, Qiang
发明人： DOI, Kunio , AOYAMA, Masahito , LI, Qiang
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T2207/30061
摘要： An automated computerized scheme for determination of the likelihood of malignancy in pulmonary nodules. The present invention includes steps of obtaining at least one computed tomography medical image (12) of a pulmonary nodule in determining if the pulmonary nodule is malignant based on the examination of seven patient (22) or image features. The method can be implemented when instructions are loaded into a computer to program the computer.
摘要翻译：一种用于确定肺结节恶性肿瘤可能性的自动计算机化方案。本发明包括以下步骤：基于七个患者22或图像特征的检查，获得肺结节的至少一个计算机断层摄影医学图像12，以确定肺结节是否恶性。当将指令加载到计算机中以对计算机进行编程时，可以实现该方法。

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