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    METHODS FOR IMPROVING THE ACCURACY IN DIFFERENTIAL DIAGNOSIS ON RADIOLOGIC EXAMINATIONS
    1.
    发明申请
    METHODS FOR IMPROVING THE ACCURACY IN DIFFERENTIAL DIAGNOSIS ON RADIOLOGIC EXAMINATIONS 审中-公开
    提高放射学检查差异诊断精度的方法

    公开(公告)号：WO9905503A3

    公开(公告)日：1999-04-15

    申请号：PCT/US9815154

    申请日：1998-07-24

    Applicant: ARCH DEV CORP

    Inventor： NISHIKAWA ROBERT M ,  JIANG YULEI ,  ASHIZAWA KAZUTO ,  DOI KUNIO

    IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  A61B5/05

    CPC classification number: G06T7/0012 ,  Y10S128/925

    Abstract: A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region.

    Abstract translation: 一种用于检测，分类和显示诸如乳房X线照片和胸片的数字医学图像中的微钙化和间质性肺病等候选异常的计算机辅助方法，用于实现该方法的程序化计算机以及用于存储所需参数的数据结构 其中，在所述分类方法中，定位了数字化医学图像中候选异常的区域，围绕所述定位候选异常中的一个或多个产生区域，从所述区域内和所述区域本身中的位置候选异常中的至少一个提取特征， 提取的特征被应用于诸如人造神经网络（ANN）的分类技术以产生分类结果（即以数字和条形图的形式的恶性的概率），并且分类结果与 用区域注释的数字化医学图像和r内的候选异常 egion。

    Methods for improving the accuracy in differential diagnosis on radiologic examinations
    3.
    发明专利
    Methods for improving the accuracy in differential diagnosis on radiologic examinations 未知

    公开(公告)号：AU8579498A

    公开(公告)日：1999-02-16

    申请号：AU8579498

    申请日：1998-07-24

    Applicant: ARCH DEV CORP

    Inventor： NISHIKAWA ROBERT M ,  JIANG YULEI ,  ASHIZAWA KAZUTO ,  DOI KUNIO

    IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00

    Abstract: A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region. In the detecting method candidate abnormalities in each of a plurality of digitized medical images are located, regions around one or more of the located candidate abnormalities in each of a plurality of digitized medical images are generated, the plurality of digitized medical images annotated with respective regions and candidate abnormalities within the regions are displayed, and a first indicator (e.g., blue arrow) is superimposed over candidate abnormalities comprising of clusters and a second indicator (e.g., red arrow) is superimposed over candidate abnormalities comprising of masses. In a user modification mode, during classification, a user modifies the located candidate abnormalities, the determined regions, and/or the extracted features, so as to modify the extracted features applied to the classification technique and the displayed results, and, during detection, a user modifies the located candidate abnormalities, the determined regions, and the extracted features, so as to modify the displayed results.

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