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Analysis of bilateral asymmetry in mammograms using directional, morphological, and density features

机译:使用方向,形态和密度特征分析乳房X线照片中的双边不对称性

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摘要

We propose techniques to analyze bilateral asymmetry in mammograms by combining directional information, morphological measures, and geometric moments related to density distributions. The procedure starts by detecting the breast boundary and the pectoral muscle edge (in mediolateral-oblique, or MLO, views). All artifacts outside the breast boundary as well as the pectoral muscle region are eliminated. A breast density model based upon a Gaussian mixture model is then used to segment the fibroglandular disks of the mammograms. Rose diagrams are used to map the magnitude and directional information related to the fibroglandular tissue filtered using multiresolution Gabor wavelets. The directional data of the left and right mammograms are aligned by using the straight lines perpendicular to the corresponding pectoral muscle edges and subtracted to obtain difference rose diagrams. Directional features are obtained from the difference rose diagrams and used to characterize the changes caused by the development of breast cancer in the form of bilateral asymmetry or architectural distortion. An additional set of features including Hu's moments, eccentricity, stretch, area, and average density are extracted from the segmented fibroglandular disks. The differences between the pairs of the features for the left and right mammograms are used as mea- sures for the analysis of asymmetry. The techniques were applied to 88 mammograms from the Mini-MIAS database. Classification accuracies of up to 84.4% were achieved, with sensitivity and specificity rates of 82.6% and 86.4%, respectively.
机译:我们提出了通过结合方向信息,形态学度量和与密度分布有关的几何矩来分析乳房X线照片中的双边不对称性的技术。该过程开始于检测乳房边界和胸肌边缘(在中斜肌或MLO视野中)。消除了乳房边界以及胸肌区域以外的所有伪影。然后使用基于高斯混合模型的乳房密度模型来分割乳房X线照片的纤维腺盘。罗斯图用于绘制与使用多分辨率Gabor小波过滤的纤维腺组织相关的大小和方向信息。通过使用垂直于相应的胸肌边缘的直线来对齐左右乳房X线照片的方向数据,并减去以得到差异玫瑰图。方向特征是从差异玫瑰图中获得的,并用于以双侧不对称或建筑变形的形式来表征由乳腺癌发展引起的变化。从分段的腓肠肌盘中提取出其他特征,包括胡的矩,偏心率,拉伸,面积和平均密度。左右乳房X线照片的特征对之间的差异用作不对称分析的量度。该技术已应用于Mini-MIAS数据库中的88幅乳房X线照片。分类准确率高达84.4%,敏感性和特异性率分别为82.6%和86.4%。

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