Automated analysis of mammographic densities
Automated analysis of mammographic densities
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DOI：
10.1088/0031-9155/41/5/007
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发表时间：
1996-05-01
影响因子：
3.5
通讯作者：
Yaffe, MJ
中科院分区：
文献类型：
--
作者：
Byng, JW;Boyd, NF;Yaffe, MJ
乳房x线照相术中乳腺实质形态的信息是判断患乳腺癌风险的最有力指标之一。为了解决乳房x光片实质的粗密度分类的一些局限性，我们一直在研究更定量、客观的方法来分析乳房x光片。这包括测量图像亮度直方图的偏度，以及用分形维数表征的图像纹理。这两种测量方法都与放射科医生对乳腺实质的主观分类有很强的相关性（Spearman相关系数，偏度和分形维数的R(s)分别为-0.88和-0.76）。此外，这两项测量都不强烈依赖于乳房x线摄影技术的模拟变化。偏度和分形测量联合使用时，与乳腺密度主观分类的相关性优于单独使用。这表明每个特征提供了一些独立的信息。
Information derived from mammographic parenchymal patterns provides one of the strongest indicators of the risk of developing breast cancer. To address several limitations of subjective classification of mammographic parenchyma into coarse density categories, we have been investigating more quantitative, objective methods of analysing the film-screen mammogram. These include measures of the skewness of the image brightness histogram, and of image texture characterized by the fractal dimension. Both measures were found to be strongly correlated with radiologists' subjective classifications of mammographic parenchyma (Spearman correlation coefficients, R(s) = -0.88 and -0.76 for skewness and fractal dimension measurements, respectively). Further, neither measure was strongly dependent on simulated changes in mammographic technique. Correlation with subjective classification of mammographic density was better when both the skewness and fractal measures were used in combination than when either was used alone. This suggests that each feature provides some independent information.