中外侧斜位乳房x线照片中胸肌的自动分割

M. Molinara, C. Marrocco, F. Tortorella
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引用次数: 15

摘要

当通过计算机辅助诊断(CAD)系统分析乳房x线照片时,胸肌的存在会影响乳房病变自动检测的结果。这个问题在中外侧斜位片(MLO)上尤其明显,在乳房x光片的边缘上,胸肌表现为一个高强度区域。由于胸肌与异常组织的特征相似,可能会干扰可疑区域的检测或影响乳房组织密度的估计,因此对胸肌的自动识别是必不可少的步骤。本文提出了一种新的乳房x线图像MLO图像中胸肌的检测方法。它是基于一个预处理步骤,有用的归一化图像和突出肌肉和乳腺组织之间的边界。随后的步骤包括边缘检测和RANSAC回归,提供肌肉区域的最终轮廓。在标准数据集上进行的实验显示了非常令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic segmentation of the pectoral muscle in mediolateral oblique mammograms
When mammograms are analyzed through a Computer Aided Diagnosis (CAD) system the presence of the pectoral muscle can affect the results of the automatic detection of breast lesions. This problem is particularly evident in mediolateral oblique (MLO) view where the pectoral muscle appears as a high intensity region across the margin of the mammogram. An automatic identification of the pectoral muscle is an essential step because of its similar characteristics with the abnormal tissue that can interfere with the detection of suspicious regions or bias the estimation of breast tissue density. This paper presents a new approach for the detection of pectoral muscle in MLO view of the mammo-graphic images. It is based on a preprocessing step useful to normalize the image and highlight the boundary between the muscle and the mammary tissue. A subsequent step including edge detection and regression via RANSAC provides the final contour of the muscle area. The experiments performed on a standard data set show very encouraging results.
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