Department of Radiological Sciences, University of California, Irvine, California 92697.
Med Phys. 2013 Dec;40(12):122305. doi: 10.1118/1.4831967.
Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study.
T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field.
The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction.
The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.
基于三维乳腺 MRI 的乳腺密度定量分析可为乳腺癌的早期检测提供有用信息。然而，组织内的偏置场会严重影响计算机图像分割过程。本研究通过对尸检标本的研究来评估偏置场对乳腺密度定量的影响。
在 1.5 T 乳腺 MRI 扫描仪上采集 20 对尸检乳房的 T1 加权图像。使用两种计算机辅助算法来定量计算乳房体积密度。首先，在存在偏置场的原始图像上使用标准模糊 C 均值（FCM）聚类进行聚类。然后，相干局部强度聚类（CLIC）方法在迭代组织分割过程中估计和校正偏置场。最后，在 CLIC 方法产生的偏置场校正图像上进行 FCM 聚类。研究了同一对乳房左右侧之间的相关性，以评估两种组织分类算法的准确性。最后，将三种方法测量的乳腺密度与从化学分析获得的金标准组织成分进行比较。使用线性相关系数 Pearson's r 评估两种图像分割算法和偏置场的影响。
CLIC 方法成功校正了偏置场引起的强度不均匀性。在左右比较中，CLIC 方法显著提高了腺体体积估计的线性拟合斜率和相关系数。左右乳房密度的相关性也从 0.93 增加到 0.98。与化学分析得到的纤维腺体体积百分比（%FGV）相比，CLIC 和 FCM 算法校正偏置场后的结果均显示出更好的线性相关性。因此，偏置场校正后 Pearson's r 从 0.86 增加到 0.92。
所研究的 CLIC 方法通过有效校正偏置场，显著提高了乳腺 MRI 图像乳腺密度定量分析的精度和准确性。预计用于乳腺密度定量的全自动计算机算法在临床 MRI 应用中具有很大的潜力。
