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首页> 外文会议>BMEI 2012;International Conference on Biomedical Engineering and Informatics >A combined method for automatic identification of the breast boundary in mammograms
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A combined method for automatic identification of the breast boundary in mammograms

机译：一种自动识别乳房X线照片中乳房边界的组合方法

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摘要

Breast region segmentation is an essential prerequisite in the (semi-)automatic analysis of digital or digitised mammographic images, which aims to separate the breast region from background information in mammograms. It normally consists of two independent segmentations, which are breast-background segmentation and pectoral muscle segmentation, respectively. The first identifies the boundary between the breast and background, and the second identifies the boundary of the pectoral muscle (present in medio-lateral oblique (MLO) views). In this paper, we propose a method for automatic identification of the breast boundary based on a combination of segmentation approaches, including histogram thresholding, edge detection, contour growing, polynomial fitting, and region growing. To demonstrate the validity of the proposed method, it is tested using two mammographic datasets: the full MIAS database and a large dataset taken from the EPIC database. For the breast-background segmentation, 98.8% and 91.5% nearly accurate results are obtained for the MIAS and EPIC data, respectively. For the pectoral muscle segmentation, 92.8% and 87.9% nearly accurate results are achieved for these two datasets. A comparison with two other methods is also provided based on the full MIAS database. These indicate the proposed method performs effectively in identifying the breast boundary in digitised mammograms. The obtained segmentation results can be used for further analysis in computer-aided diagnosis.
机译：乳房区域分割是数字化或数字化乳腺X线图像（半）自动分析的基本前提，其目的是将乳房区域与乳腺X线照片中的背景信息区分开。它通常包括两个独立的分割，分别是乳房背景分割和胸肌分割。第一个标识乳房和背景之间的边界，第二个标识胸肌的边界（以中外侧斜（MLO）视图显示）。在本文中，我们提出了一种基于分割方法的自动识别乳房边界的方法，该方法包括直方图阈值化，边缘检测，轮廓增长，多项式拟合和区域增长。为了证明该方法的有效性，使用了两个乳腺X线摄影数据集进行了测试：完整的MIAS数据库和从EPIC数据库获取的大型数据集。对于乳腺背景分割，分别针对MIAS和EPIC数据可获得98.8％和91.5％的近乎准确的结果。对于胸肌分割，这两个数据集的准确率分别为92.8％和87.9％。还基于完整的MIAS数据库提供了与其他两种方法的比较。这些表明所提出的方法可以有效地识别数字化乳房X线照片中的乳房边界。获得的分割结果可用于计算机辅助诊断的进一步分析。

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