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    • 8. 发明申请
    • BREAST TISSUE DENSITY MEASURE
    • 乳腺组织密度测量
    • WO2007090892A1
    • 2007-08-16
    • PCT/EP2007/051284
    • 2007-02-09
    • NORDIC BIOSCIENCE A/SRAUNDAHL, JakobLOOG, MarcoNIELSEN, Mads
    • RAUNDAHL, JakobLOOG, MarcoNIELSEN, Mads
    • G06K9/52G06K9/62
    • G06K9/527G06K9/6223
    • Mammogram images are processed by computer to derive automatically a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being a parameter that changes alongside changes in breast density and is hence useful in assessing cancer risk. The method comprises the steps of processing each image of at least part of a breast by: computing for pixels of the image a quotient value representative of the aspect ratio of tissue structures depicted in the image; using a trained classifier to classify said pixels according to their respective said quotient values and assigning a score to the respective pixels representing their classification with respect to at least two classes; deriving said parameter that changes alongside changes in breast density based on the aggregate pixel membership scores of said classes. The classifier may be trained either by unsupervised learning or by supervised learning.
    • 计算机处理乳房X线照片图像以自动导出用于检测同一乳房的后续图像或相对于这样的图像的对照组的乳房组织中的差异的参数的值,所述导出参数是随乳房变化而变化的参数 密度,因此有助于评估癌症风险。 该方法包括以下步骤:通过以下步骤处理乳房的至少一部分的每个图像:计算图像中像素的代表图像中描绘的组织结构的纵横比的商值; 使用经过训练的分类器根据其相应的商值对所述像素进行分类,并且将分数分配给相对于至少两个类别表示其分类的各个像素; 导出所述参数随着所述类别的聚集像素成分分数而随着乳房密度的变化而变化。 分类器可以通过无监督学习或受监督学习进行训练。