Department of Radiology, University of Michigan, Ann Arbor Michigan 48109, USA.
Med Phys. 2010 Jan;37(1):391-401. doi: 10.1118/1.3273062.
Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms.
A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB-Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB-Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM).
In comparison with the authors' previously developed gradient-based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test (p < 0.0001).
The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.
乳腺边界的自动检测是计算机辅助分析乳房 X 线照片的基本步骤之一。在这项研究中，作者开发了一种新的基于动态多阈值的乳腺边界（MTBB）检测方法，用于数字化乳房 X 线照片。
使用了来自机构审查委员会批准项目的连续病例的 716 张屏-片 mammograms（442 CC 视图和 274 MLO 视图）的大型数据集。一位经验丰富的乳腺放射科医生使用图形界面手动跟踪每个数字化图像的乳腺边界，以提供参考标准。通过动态调整阈值到乳腺周边局部区域的灰度范围，获得初始乳腺边界（MTBB-Initial）。然后，使用水平和垂直 Sobel 滤波的梯度信息对初始乳腺边界进行细化，得到最终的乳腺边界（MTBB-Final）。通过与参考标准比较，使用三个性能指标评估乳腺边界检测算法的准确性：Hausdorff 距离（HDist）、平均最小欧几里得距离（AMinDist）和面积重叠度量（AOM）。
与作者之前开发的基于梯度的乳腺边界（GBB）算法相比，发现对于 GBB、MTBB-Initial 和 MTBB-Final，分别有 68%、85%和 94%的图像的 Hdist 误差小于 6 像素（4.8 毫米）。对于 GBB、MTBB-Initial 和 MTBB-Final，分别有 89%、90%和 96%的图像的 AMinDist 误差小于 1.5 像素（1.2 毫米）。对于 GBB、MTBB-Initial 和 MTBB-Final，分别有 96%、98%和 99%的图像的 AOM 值大于 0.9。通过 Wilcoxon 符号秩检验，MTBB-Final 方法在所有评估指标上的改进均具有统计学意义（p<0.0001）。
结合动态多阈值和梯度信息的 MTBB 方法比主要使用梯度信息的乳腺边界检测算法具有更好的性能。
