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首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >Classification of breast masses and normal tissues in digital tomosynthesis mammography
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Classification of breast masses and normal tissues in digital tomosynthesis mammography

机译：数字断层合成乳腺X线摄影术中乳腺肿块和正常组织的分类

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Digital tomosynthesis mammography (DTM) can provide quasi-3D structural information of the breast by reconstructing the breast volume from projection views (PV) acquired in a limited angular range. Our purpose is to design an effective classifier to distinguish breast masses from normal tissues in DTMs. A data set of 100 DTM cases collected with a GE first generation prototype DTM system at the Massachusetts General Hospital was used. We reconstructed the DTMs using a simultaneous algebraic reconstruction technique (SART). Mass candidates were identified by 3D gradient field analysis. Three approaches to distinguish breast masses from normal tissues were evaluated. In the 3D approach, we extracted morphological and run-length statistics texture features from DTM slices as input to a linear discriminant analysis (LDA) classifier. In the 2D approach, the raw input PVs were first preprocessed with a Laplacian pyramid multi-resolution enhancement scheme. A mass candidate was then forward-projected to the preprocessed PVs in order to determine the corresponding regions of interest (ROIs). Spatial gray-level dependence (SGLD) texture features were extracted from each ROI and averaged over 11 PVs. An LDA classifier was designed to distinguish the masses from normal tissues. In the combined approach, the LDA scores from the 3D and 2D approaches were averaged to generate a mass likelihood score for each candidate. The A_z values were 0.87±0.02, 0.86±0.02, and 0.91±0.02 for the 3D, 2D, and combined approaches, respectively. The difference between the A_z values of the 3D and 2D approaches did not achieve statistical significance. The performance of the combined approach was significantly (p < 0.05) better than either the 3D or 2D approach alone. The combined classifier will be useful for false-positive reduction in computerized mass detection in DTM.
机译：通过在有限的角度范围内获取的投影视图（PV）重建乳房体积，数字断层摄影X线断层扫描（DTM）可以提供乳房的准3D结构信息。我们的目的是设计一种有效的分类器，以区分DTM中乳腺肿块与正常组织。使用在马萨诸塞州总医院使用GE第一代原型DTM系统收集的100个DTM病例数据集。我们使用同时​​代数重建技术（SART）重建了DTM。通过3D梯度场分析确定了候选质量。评价了三种区分乳房肿块和正常组织的方法。在3D方法中，我们从DTM切片中提取了形态学和游程统计纹理特征，作为线性判别分析（LDA）分类器的输入。在二维方法中，首先使用拉普拉斯金字塔多分辨率增强方案对原始输入PV进行预处理。然后将大量候选对象正投影到预处理的PV上，以确定相应的关注区域（ROI）。从每个ROI提取空间灰度相关性（SGLD）纹理特征，并平均11个PV。 LDA分类器旨在区分正常组织和肿块。在组合方法中，将3D和2D方法的LDA得分平均，以生成每个候选者的质量似然得分。对于3D，2D和组合方法，A_z值分别为0.87±0.02、0.86±0.02和0.91±0.02。 3D和2D方法的A_z值之间的差异未达到统计意义。组合方法的性能比单独的3D或2D方法显着好（p <0.05）。组合的分类器对于DTM中计算机质量检测的假阳性减少很有用。

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