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    • 1. 发明申请
    • Automated method and system for the detection of lung nodules in low-dose CT image for lung-cancer screening
    • 用于肺癌筛查的低剂量CT图像中肺结节检测的自动化方法和系统
    • US20050171409A1
    • 2005-08-04
    • US10767342
    • 2004-01-30
    • Hidetaka ArimuraFeng LiJunji ShiraishiKunio Doi
    • Hidetaka ArimuraFeng LiJunji ShiraishiKunio Doi
    • G06T7/00G21K1/12H05G1/60
    • G06T7/0012G06T2207/30061
    • A method, system, and computer program product for detecting at least one nodule in a medical image of a subject, including identifying, in the medical image, an anatomical region corresponding to at least a portion of an organ of interest; filtering the medical image to obtain a difference image; detecting, in the difference image, a first plurality of nodule candidates within the anatomical region; calculating respective nodule feature values of the first plurality of nodule candidates based on pixel values of at least one of the medical image and the difference image; removing false positive nodule candidates from the first plurality of nodule candidates based on the respective nodule feature values to obtain a second plurality of nodule candidates; and determining the at least one nodule by classifying each of the second plurality of nodule candidates as a nodule or a non-nodule based on at least one of the pixel values and the respective nodule feature values. True-positive nodules are identified using linear discriminant analysis and/or a Multi-MTANN.
    • 一种用于检测受试者的医学图像中的至少一个结节的方法,系统和计算机程序产品,包括在医学图像中识别对应于感兴趣器官的至少一部分的解剖区域; 过滤医学图像以获得差异图像; 在所述差分图像中检测所述解剖区域内的第一多个结节候选物; 基于所述医学图像和所述差分图像中的至少一个的像素值来计算所述第一多个结节候选的各个结节特征值; 基于各个结节特征值从所述第一多个结节候选中去除假阳性结节候选,以获得第二多个结节候选; 以及基于像素值和相应结节特征值中的至少一个,将所述第二多个结节候选中的每一个分类为结节或非结节来确定所述至少一个结节。 使用线性判别分析和/或多MTANN识别真阳性结节。
    • 3. 发明授权
    • Automated method and system for the detection of lung nodules in low-dose CT images for lung-cancer screening
    • 用于肺癌筛查的低剂量CT图像中肺结节检测的自动化方法和系统
    • US07305111B2
    • 2007-12-04
    • US10767342
    • 2004-01-30
    • Hidetaka ArimuraFeng LiJunji ShiraishiKunio Doi
    • Hidetaka ArimuraFeng LiJunji ShiraishiKunio Doi
    • G21K1/12G06K9/00
    • G06T7/0012G06T2207/30061
    • A method, system, and computer program product for detecting at least one nodule in a medical image of a subject, including identifying, in the medical image, an anatomical region corresponding to at least a portion of an organ of interest; filtering the medical image to obtain a difference image; detecting, in the difference image, a first plurality of nodule candidates within the anatomical region; calculating respective nodule feature values of the first plurality of nodule candidates based on pixel values of at least one of the medical image and the difference image; removing false positive nodule candidates from the first plurality of nodule candidates based on the respective nodule feature values to obtain a second plurality of nodule candidates; and determining the at least one nodule by classifying each of the second plurality of nodule candidates as a nodule or a non-nodule based on at least one of the pixel values and the respective nodule feature values. True-positive nodules are identified using linear discriminant analysis and/or a Multi-MTANN.
    • 一种用于检测受试者的医学图像中的至少一个结节的方法,系统和计算机程序产品,包括在医学图像中识别对应于感兴趣器官的至少一部分的解剖区域; 过滤医学图像以获得差异图像; 在所述差分图像中检测所述解剖区域内的第一多个结节候选物; 基于所述医学图像和所述差分图像中的至少一个的像素值来计算所述第一多个结节候选的各个结节特征值; 基于各个结节特征值从所述第一多个结节候选中去除假阳性结节候选,以获得第二多个结节候选; 以及基于像素值和相应结节特征值中的至少一个,将所述第二多个结节候选中的每一个分类为结节或非结节来确定所述至少一个结节。 使用线性判别分析和/或多MTANN识别真阳性结节。
    • 5. 发明申请
    • Method for detection of abnormalities in three-dimensional imaging data
    • 三维成像数据异常检测方法
    • US20050259854A1
    • 2005-11-24
    • US10849807
    • 2004-05-21
    • Hidetaka ArimuraQiang LiKunio Doi
    • Hidetaka ArimuraQiang LiKunio Doi
    • G06K9/00G06K9/40G06T5/00G06T5/30G06T7/00
    • G06T7/0012G06T5/003G06T7/11G06T7/155G06T2207/10072G06T2207/20044G06T2207/20164G06T2207/30101
    • 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.
    • 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。