公开(公告)号：US06891964B2

公开(公告)日：2005-05-10

申请号：US09990310

申请日：2001-11-23

申请人： Kunio Doi ,  Masahito Aoyama ,  Qiang Li

发明人： Kunio Doi ,  Masahito Aoyama ,  Qiang Li

IPC分类号： G06T7/00 ,  G06K9/00 ,  G06K9/40 ,  G06K9/48

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 of a pulmonary nodule in determining if the pulmonary nodule is malignant based on the examination of seven patient or image features. The method can be implemented when instructions are loaded into a computer to program the computer. The significance of employing seven patient or image features is that statistically, seven features are the most practical based on the unique implementation of statistical analysis. Out of the seven features that are now analyzed to determine if a pulmonary nodule is malignant, these features are selected to optimize the accuracy of the diagnosis of a pulmonary nodule. Through a unique sampling scheme, different embodiments of the present invention utilize different combinations of features to optimize the accuracy of the method of the present invention.

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：基于七个患者或图像特征的检查，在确定肺结节是否恶性时获得肺结节的至少一个计算机断层摄影医学图像。 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。 采用七个患者或图像特征的意义在统计学上，七个特征是最实际的，基于统计分析的独特实现。 在现在分析以确定肺结节是否恶性的七个特征中，选择这些特征以优化肺结节诊断的准确性。 通过独特的采样方案，本发明的不同实施例利用不同的特征组合来优化本发明的方法的精度。

公开(公告)号：US06694046B2

公开(公告)日：2004-02-17

申请号：US09818831

申请日：2001-03-28

申请人： Kunio Doi ,  Masahito Aoyama

发明人： Kunio Doi ,  Masahito Aoyama

IPC分类号： G06K900

CPC分类号： G06T7/0012

摘要： An automated method for analyzing a nodule and a computer storage medium storing computer instructions by which the method can be implemented when the instructions are loaded into a computer to program the computer. The method includes obtaining a digital image including the nodule; segmenting the nodule to obtain an outline of the nodule, including generating a difference image from chest image, identifying image intensity contour lines representative of respective image intensities in a region of interest including the nodule, and obtaining an outline of the nodule based on the image intensity contours; extracting features of the nodule based on the outline; applying features including the extracted features to at least one image classifier; and determining a likelihood of malignancy of the nodule based on the output of the at least one classifier. In one embodiment, extracted features are applied to a linear discriminant analyzer and/or an artificial neural network analyzer, the outputs of which are thresholded and the nodule determined to be non-malignant if each classifier output is below the threshold. In another embodiment, a common nodule appearing in an x-ray chest image and a CT image is segmented in each image, features extracted based on the outlines of each segmented nodule in the respective x-ray chest and CT images, and the extracted features from the x-ray chest image and CT images merged as inputs to a common classifier, with the output of the common classifier indicating the likelihood of malignancy.

摘要翻译： 一种用于分析结节的自动化方法和存储计算机指令的计算机存储介质，当指令被加载到计算机中以对计算机进行编程时，可以通过该方法实现该方法。 该方法包括获得包括结节的数字图像; 分割结节以获得结节的轮廓，包括从胸部图像生成差异图像，识别表示包括结节的感兴趣区域中的各个图像强度的图像强度轮廓线，以及基于图像获得结节的轮廓 强度轮廓; 根据轮廓提取结节的特征; 将包括提取的特征的特征应用于至少一个图像分类器; 以及基于所述至少一个分类器的输出确定所述结节恶性的可能性。 在一个实施例中，提取的特征被应用于线性判别分析器和/或人造神经网络分析器，其输出被阈值化，并且如果每个分类器输出低于阈值，则确定为非恶性的结节。 在另一个实施例中，在每个图像中分割出现在x射线胸部图像和CT图像中的共同结节，基于各个X射线胸部和CT图像中的每个分段结节的轮廓提取的特征，以及提取的特征 从X射线胸部图像和CT图像合并为公共分类器的输入，公共分类器的输出指示恶性肿瘤的可能性。

公开(公告)号：US07043066B1

公开(公告)日：2006-05-09

申请号：US09830562

申请日：1999-11-05

申请人： Kunio Doi ,  Qiang Li ,  Shigehiko Katsuragawa ,  Takayuki Ishida

发明人： Kunio Doi ,  Qiang Li ,  Shigehiko Katsuragawa ,  Takayuki Ishida

IPC分类号： G06K9/00

CPC分类号： G06T5/50 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/174 ,  G06T7/33 ,  G06T2207/10116 ,  G06T2207/20224 ,  G06T2207/30064

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： 一种用于胸部图像的计算机化处理的方法，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（S 300）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（S 400）; 以及从所述另一图像中减去所述翘曲图像以生成减法图像（S 600）。 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; 并且移动旋转的图像以产生以横向倾斜校正图像为中心的中线的横向倾斜校正（S 200）第二图像。

公开(公告)号：US06937776B2

公开(公告)日：2005-08-30

申请号：US10355147

申请日：2003-01-31

申请人： Qiang Li ,  Kunio Doi

发明人： Qiang Li ,  Kunio Doi

IPC分类号： G06F9/40 ,  G06K9/00 ,  G06K9/52 ,  G06T5/00 ,  G06T5/20 ,  G06T7/00

CPC分类号： G06K9/527 ,  G06T5/002 ,  G06T5/003 ,  G06T5/20 ,  G06T7/0012 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/20132 ,  G06T2207/30064

摘要： A method, system, and computer program product for evaluating an image including an object, including filtering image data derived from the image with a first geometric enhancement filter having magnitude and likelihood filter components so as to produce first filtered image data in which a first geometric pattern is enhanced. Thereafter the filtered image data can be subjected to processing to derive a measure indicative of the presence of the object in the image, including determining a region of interest in the image, extracting at least one feature from the first filtered image data from within the region of interest, and applying the at least one extracted feature to a classifier configured to output the measure indicative of the presence of the object in the image. The image data can also be subjected to filtering with second and/or third geometric filters which enhance different geometric patterns, and which produce respective filtered data which are also processed to derive the measure indicative of the presence of the object.

摘要翻译： 一种用于评估包括对象的图像的方法，系统和计算机程序产品，包括用具有幅度和似然滤波器分量的第一几何增强滤波器对从图像导出的图像数据进行滤波，以便产生第一滤波图像数据，其中第一几何 模式得到增强。 此后，滤波图像数据可以进行处理以导出指示图像中对象的存在的度量，包括确定图像中的感兴趣区域，从区域内的第一滤波图像数据中提取至少一个特征 感兴趣的，并且将所述至少一个提取的特征应用于被配置为输出指示所述图像中的对象的存在的度量的分类器。 图像数据也可以用第二和/或第三几何滤波器进行滤波，该第二和/或第三几何滤波器增强不同的几何图形，并产生相应的滤波数据，这些数据也被处理以导出指示对象存在的测量。

公开(公告)号：US06683973B2

公开(公告)日：2004-01-27

申请号：US10231064

申请日：2002-08-30

申请人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

发明人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

IPC分类号： G06K900

CPC分类号： G06T7/0012

摘要： A method to determine whether a candidate abnormality in a medical digital image is an actual abnormality, a system which implements the method, and a computer readable medium which stores program steps to implement the method, wherein the method includes obtaining a medical digital image including a candidate abnormality; obtaining plural first templates and plural second templates respectively corresponding to predetermined abnormalities and predetermined non-abnormalities; comparing the candidate abnormality with the obtained first and second templates to derive cross-correlation values between the candidate abnormality and each of the obtained first and second templates; determining the largest cross-correlation value derived in the comparing step and whether the largest cross-correlation value is produced by comparing the candidate abnormality with the first templates or with the second templates; and determining the candidate abnormality to be an actual abnormality when the largest cross-correlation value is produced by comparing the candidate abnormality with the first templates and determining the candidate abnormality to be a non-abnormality when the largest cross-correlation value is produced by comparing the candidate abnormality with the second templates. An actual abnormality is similarly classified as malignant or benign based on further cross-correlation values obtained by comparisons with additional templates corresponding to malignant and benign abnormalities.

摘要翻译： 一种确定医疗数字图像中的候选异常是否为实际异常的方法，实现该方法的系统以及存储执行该方法的程序步骤的计算机可读介质，其中，所述方法包括：获得包括 候选异常; 分别对应于预定的异常和预定的非异常获得多个第一模板和多个第二模板; 将所述候选异常与所获得的第一和第二模板进行比较，以导出候选异常与获得的第一和第二模板中的每一个之间的互相关值; 确定在比较步骤中导出的最大互相关值，以及通过将候选异常与第一模板或第二模板进行比较来产生最大互相关值; 以及当通过将所述候选异常与所述第一模板进行比较而产生所述最大互相关值时，将所述候选异常判定为实际异常，并且当通过比较产生所述最大互相关值时将所述候选异常确定为非异常 候选异常与第二个模板。 基于通过与对应于恶性和良性异常的附加模板的比较获得的进一步互相关值，实际异常类似地分类为恶性或良性。

公开(公告)号：US06594378B1

公开(公告)日：2003-07-15

申请号：US09692218

申请日：2000-10-20

申请人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

发明人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

IPC分类号： G06K900

CPC分类号： G06T3/0068 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/149 ,  G06T7/174 ,  G06T7/344 ,  G06T2207/20061 ,  G06T2207/30008 ,  G06T2207/30061 ,  Y10S128/922

摘要： A method, system and computer readable medium of computerized processing of chest images including obtaining digital first and second images of a chest and detecting rib edges in at least one of the first and second images. The rib edges are detected by correlating points in the at least one of the first and second images to plural rib edge models using a Hough transform to identify approximate rib edges in one of the images, and delineating actual rib edges derived from the identified approximate rib edges using a snake model. The method system and computer readable medium further include deriving the shift values using the actual rib edges and warping one of the first and second images to produce a warped image which is registered to the other of the first and second images based at least in part on the shift values.

摘要翻译： 一种用于胸部图像的计算机化处理的方法，系统和计算机可读介质，包括获得胸部的数字第一和第二图像以及检测第一和第二图像中的至少一个中的肋边缘。 通过使用霍夫变换将第一和第二图像中的至少一个图像中的点与多个肋边缘模型相关联来检测肋边缘，以识别其中一个图像中的近似肋边缘，并且描绘从识别的近似肋导出的实际肋边缘 边缘使用蛇模型。 方法系统和计算机可读介质进一步包括使用实际的肋边缘导出移位值，并使第一和第二图像之一变形，以产生至少部分地基于第一和第二图像被注册到第一和第二图像中的另一个的扭曲图像 移位值。

公开(公告)号：US06470092B1

公开(公告)日：2002-10-22

申请号：US09716335

申请日：2000-11-21

申请人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

发明人： Qiang Li ,  Shigehiko Katsuragawa ,  Kunio Doi

IPC分类号： G06K900

CPC分类号： G06T7/0012

摘要： A method to determine whether a candidate abnormality in a medical digital image is an actual abnormality, a system which implements the method, and a computer readable medium which stores program steps to implement the method, wherein the method includes obtaining a medical digital image including a candidate abnormality; obtaining plural first templates and plural second templates respectively corresponding to predetermined abnormalities and predetermined non-abnormalities; comparing the candidate abnormality with the obtained first and second templates to derive cross-correlation values between the candidate abnormality and each of the obtained first and second templates; determining the largest cross-correlation value derived in the comparing step and whether the largest cross-correlation value is produced by comparing the candidate abnormality with the first templates or with the second templates; and determining the candidate abnormality to be an actual abnormality when the largest cross-correlation value is produced by comparing the candidate abnormality with the first templates and determining the candidate abnormality to be a non-abnormality when the largest cross-correlation value is produced by comparing the candidate abnormality with the second templates. An actual abnormality is similarly classified as malignant or benign based on further cross-correlation values obtained by comparisons with additional templates corresponding to malignant and benign abnormalities.

摘要翻译： 一种确定医疗数字图像中的候选异常是否为实际异常的方法，实现该方法的系统以及存储执行该方法的程序步骤的计算机可读介质，其中，所述方法包括：获得包括 候选异常; 分别对应于预定的异常和预定的非异常获得多个第一模板和多个第二模板; 将所述候选异常与所获得的第一和第二模板进行比较，以导出候选异常与获得的第一和第二模板中的每一个之间的互相关值; 确定在比较步骤中导出的最大互相关值，以及通过将候选异常与第一模板或第二模板进行比较来产生最大互相关值; 以及当通过将所述候选异常与所述第一模板进行比较而产生所述最大互相关值时，将所述候选异常判定为实际异常，并且当通过比较产生所述最大互相关值时将所述候选异常确定为非异常 候选异常与第二个模板。 基于通过与对应于恶性和良性异常的附加模板的比较获得的进一步互相关值，实际异常类似地分类为恶性或良性。

公开(公告)号：US20080298657A1

公开(公告)日：2008-12-04

申请号：US12094161

申请日：2006-11-22

申请人： Junji Shiraishi ,  Kunio Doi ,  Daniel Appelbaum ,  Yonglin Pu ,  Qiang Li

发明人： Junji Shiraishi ,  Kunio Doi ,  Daniel Appelbaum ,  Yonglin Pu ,  Qiang Li

IPC分类号： G06K9/00

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 (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.

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归一化（1：140）。 使用多段匹配方法（1：160），其中一个图像被扭曲以匹配其他图像。 从第二图像中减去第一图像以形成减影图像（1:220）。 最后，显示所得的减法图像。

公开(公告)号：US20050259854A1

公开(公告)日：2005-11-24

申请号：US10849807

申请日：2004-05-21

申请人： Hidetaka Arimura ,  Qiang Li ,  Kunio Doi

发明人： Hidetaka Arimura ,  Qiang Li ,  Kunio Doi

IPC分类号： G06K9/00 ,  G06K9/40 ,  G06T5/00 ,  G06T5/30 ,  G06T7/00

CPC分类号： G06T7/0012 ,  G06T5/003 ,  G06T7/11 ,  G06T7/155 ,  G06T2207/10072 ,  G06T2207/20044 ,  G06T2207/20164 ,  G06T2207/30101

摘要： A method, system, and computer program product for determining existence of an abnormality in a medical image, including (1) obtaining volume image data corresponding to the medical image; (2) filtering the volume image data using an enhancement filter to produce a filtered image in which a predetermined pattern is enhanced; (3) detecting, in the filtered image, a first plurality of abnormality candidates using multiple gray-level thresholding; (4) grouping, based on size and local structures, the first plurality of abnormality candidates into a plurality of abnormality classes; (5) removing false positive candidates from each abnormality class based on class-specific image features to produce a second plurality of abnormality candidates; and (6) applying the at least one abnormality to a classifier and classifying each candidate in the second plurality of abnormality candidates as a false positive candidate or an abnormality.

摘要翻译： 一种用于确定医学图像中的异常的存在的方法，系统和计算机程序产品，包括（1）获得与医学图像相对应的体积图像数据; （2）使用增强滤波器对体积图像数据进行滤波，以产生增强了预定图案的滤波图像; （3）在滤波图像中，使用多灰度阈值处理检测第一多个异常候选; （4）基于大小和局部结构将所述第一多个异常候选分组成多个异常类别; （5）基于类特定图像特征从每个异常等级去除假阳性候选以产生第二多个异常候选; 以及（6）将所述至少一个异常应用于分类器并将所述第二多个异常候选中的每个候选者分类为假阳性候选或异常。

公开(公告)号：US06205348B1

公开(公告)日：2001-03-20

申请号：US09298852

申请日：1999-04-26

申请人： Maryellen L. Giger ,  Kunio Doi

发明人： Maryellen L. Giger ,  Kunio Doi

IPC分类号： A61B505

CPC分类号： G06T7/0012 ,  A61B6/4042 ,  A61B6/482 ,  A61B6/505 ,  A61B6/583 ,  G06T2207/10116 ,  G06T2207/30008 ,  G06T2207/30012

摘要： A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques including texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both bone mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae. These features are then merged using artificial neural networks in order to yield a likelihood of risk of future fracture. In addition, a method and system is presented in which dual-energy imaging techniques are used to yield measures of both bone mass and bone structure with one low-dose radiographic examination; thus, making the system desirable for screening (for osteoporosis and risk of future fracture).

摘要翻译： 一种计算机化方法和系统，用于骨骼结构的射线照相分析和未来骨折的风险，有或没有骨量的测量。 包括用于量化骨骼结构和未来骨折风险的纹理分析的技术。 骨结构的纹理分析包括方向性信息，例如在感兴趣的骨区域中的ROI的RMS变化和ROI的功率谱的第一时刻的角度依赖性方面。 该系统还包括使用双能量成像，以便通过一次检查获得骨量和骨骼结构的测量。 给出了常规脊柱X光照片对椎体内部区域进行分析的具体应用。 技术包括表征骨骼结构的功率谱的新特征，并且允许提取用于表征骨小梁的空间分布和厚度的方向性特征。 然后使用人工神经网络将这些特征合并，以产生未来骨折风险的可能性。 此外，提出了一种方法和系统，其中使用双能量成像技术通过一次低剂量射线照相检查来产生骨量和骨结构的测量; 因此，使得该系统对于筛选（对于骨质疏松症和未来骨折的风险）是理想的。