Objective models of compressed breast shapes undergoing mammography
Objective models of compressed breast shapes undergoing mammography
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DOI：
10.1118/1.4789579
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发表时间：
2013-03-01
期刊：
影响因子：
3.8
通讯作者：
Sechopoulos, Ioannis
中科院分区：
文献类型：
--
作者：
Feng, Steve Si Jia;Patel, Bhavika;Sechopoulos, Ioannis
目的：建立基于客观分析的乳房x线摄影压缩乳房模型，该模型能够在获得的临床图像中准确地表示乳房形状，并产生新的临床逼真的形状。方法：采用一种自动边缘检测算法，从大型数字乳房摄影图像数据库中对临床获得的颅尾侧（CC）和中外侧斜（MLO）视图乳房图像进行乳房形状分类。对这些形状进行主成分分析（PCA），将形状中包含的信息减少到少数线性自变量。乳房形状模型，每个视图中的一个，是从确定的主成分中开发出来的，通过计算平均距离误差（ADE），通过视觉和定量地评估它们从一组独立的乳房x光片中重现乳房形状的能力，而不是在PCA中使用。结果：基于6个主成分（分别占数据集总方差的99.2%和98.0%）的CC和MLO乳房x线片的PCA乳房形状模型能够以高保真度再现乳房形状（CC视图平均ADE = 0.90 mm， MLO视图平均ADE = 1.43 mm），并生成新的临床真实形状。基于较少主成分的PCA模型也很成功，但程度较低，因为双成分模型在CC视图中显示平均ADE = 2.99 mm，在MLO视图中显示平均ADE = 4.63 mm。四分量模型显示CC视图的平均ADE = 1.47 mm， MLO视图的平均ADE = 2.14 mm。对模型间各图像的ADE值进行配对t检验，差异有统计学意义（最大p值= 0.0247）。对模型乳房形状的目视检查证实了这些结果。与六个主成分相关的主成分参数的直方图拟合为高斯分布。使用这些分布的平均PCA参数值和基于拟合的高斯分布随机生成的值，六分量模型也被用于生成CC和MLO视图乳房形状，这些乳房形状与临床遇到的乳房相似。提供了一个电子表格，其中包含应用该模型所需的数据，作为补充材料。结论：我们的乳房形状的PCA模型在两个乳房x线照片成功地再现分析的乳房形状，并产生新的临床相关的形状。这项工作可以帮助研究应用，包括乳房形状建模，如x射线散射校正，剂量学，和图像配准。(C) 2013年美国医学物理学家协会。(http: / ldx.doi.org/10.1118/1.4789579)
Purpose: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes.Methods: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE).Results: The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 lima for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material.Conclusions: Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration. (C) 2013 American Association of Physicists in Medicine. [http:/ldx.doi.org/10.1118/1.4789579]