Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842, USA.
Med Phys. 2010 May;37(5):2289-99. doi: 10.1118/1.3395576.
To develop a new texture-field orientation (TFO) method that combines a priori knowledge, local and global information for the automated identification of pectoral muscle on mammograms.
The authors designed a gradient-based directional kernel (GDK) filter to enhance the linear texture structures, and a gradient-based texture analysis to extract a texture orientation image that represented the dominant texture orientation at each pixel. The texture orientation image was enhanced by a second GDK filter for ridge point extraction. The extracted ridge points were validated and the ridges that were less likely to lie on the pectoral boundary were removed automatically. A shortest-path finding method was used to generate a probability image that represented the likelihood that each remaining ridge point lay on the true pectoral boundary. Finally, the pectoral boundary was tracked by searching for the ridge points with the highest probability lying on the pectoral boundary. A data set of 130 MLO-view digitized film mammograms (DFMs) from 65 patients was used to train the TFO algorithm. An independent data set of 637 MLO-view DFMs from 562 patients was used to evaluate its performance. Another independent data set of 92 MLO-view full field digital mammograms (FFDMs) from 92 patients was used to assess the adaptability of the TFO algorithm to FFDMs. The pectoral boundary detection accuracy of the TFO method was quantified by comparison with an experienced radiologist's manually drawn pectoral boundary using three performance metrics: The percent overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist).
The mean and standard deviation of POA, Hdist, and AvgDist were 95.0 +/- 3.6%, 3.45 +/- 2.16 mm, and 1.12 +/- 0.82 mm, respectively. For the POA measure, 91.5%, 97.3%, and 98.9% of the computer detected pectoral muscles had POA larger than 90%, 85%, and 80%, respectively. For the distance measures, 85.4% and 98.0% of the computer detected pectoral boundaries had Hdist within 5 and 10 mm, respectively, and 99.4% of computer detected pectoral muscle boundaries had AvgDist within 5 mm from the radiologist's manually drawn boundaries.
The pectoral muscle on DFMs can be detected accurately by the automated TFO method. The preliminary study of applying the same pectoral muscle identification algorithm to FFDMs without retraining demonstrates that the TFO method is reasonably robust against the differences in the image properties between the digitized and digital mammograms.
开发一种新的纹理场方向（TFO）方法，该方法结合了先验知识、局部和全局信息，用于自动识别乳房 X 光片中的胸肌。
作者设计了一种基于梯度的定向核（GDK）滤波器来增强线性纹理结构，并进行基于梯度的纹理分析以提取纹理方向图像，该图像代表每个像素的主导纹理方向。使用第二个 GDK 滤波器增强纹理方向图像以提取脊点。自动验证提取的脊点，并去除不太可能位于胸肌边界上的脊线。使用最短路径查找方法生成概率图像，该图像表示每个剩余脊点位于真实胸肌边界上的可能性。最后，通过搜索具有最高概率位于胸肌边界上的脊点来跟踪胸肌边界。使用来自 65 名患者的 130 张 MLO 视图数字化胶片 mammogram（DFM）数据集来训练 TFO 算法。使用来自 562 名患者的 637 张 MLO 视图 DFM 数据集独立评估其性能。使用来自 92 名患者的 92 张 MLO 视图全视野数字 mammogram（FFDM）数据集评估 TFO 算法对 FFDM 的适应性。通过与经验丰富的放射科医生手动绘制的胸肌边界进行比较，使用三个性能指标（重叠百分比面积（POA）、Hausdorff 距离（Hdist）和平均距离（AvgDist））量化 TFO 方法的胸肌边界检测准确性。
POA、Hdist 和 AvgDist 的平均值和标准差分别为 95.0 +/- 3.6%、3.45 +/- 2.16 mm 和 1.12 +/- 0.82 mm。对于 POA 测量，91.5%、97.3%和 98.9%的计算机检测到的胸肌具有大于 90%、85%和 80%的 POA。对于距离测量，85.4%和 98.0%的计算机检测到的胸肌边界的 Hdist 在 5 和 10mm 以内，99.4%的计算机检测到的胸肌边界的 AvgDist 在放射科医生手动绘制的边界的 5mm 以内。
通过自动 TFO 方法可以准确检测 DFM 上的胸肌。初步研究表明，无需重新训练即可将相同的胸肌识别算法应用于 FFDM，这表明 TFO 方法对数字化和数字 mammogram 之间图像特性的差异具有相当的鲁棒性。
