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首页> 外文期刊>Journal of Digital Imaging >Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature
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Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature

机译：使用平均梯度和基于形状的特征自动检测胸肌

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摘要

In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its’ simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches.
机译：在乳房X光检查的中外侧斜视图中，由于胸肌与异常组织的相似特征，有时胸膜肌肉可能会影响乳腺癌的检测。因此，在检测乳腺癌时应分别处理胸肌。本文提出了一种基于平均梯度和基于形状的特征检测胸肌的新方法。该过程首先使用平均梯度，位置和形状的胸肌特征将胸肌边界近似为一条直线。然后将直线调整为平滑的曲线，该曲线可以更准确地代表胸廓边缘。最后，产生一个封闭区域，该区域代表胸肌作为分割蒙版。该方法的主要优点是它的简单性和准确性。该方法应用于200幅乳腺图像，其中包括80幅来自乳腺图像分析协会（mini-MIAS）数据库的随机选择的扫描胶片图像，80幅直接X射线照相（DR）图像和40幅来自本地数据库的计算机X射线照相（CR）图像。基于假阳性（FP），假阴性（FN）像素百分比和平均距离最近点（MDCP）评估性能。考虑到所有图像，mini-MIAS，DR和CR图像的平均FP和FN像素百分比分别为4.22％，3.93％，18.81％和6.71％，6.28％，5.12％。对于同一组数据库，获得的MDCP值分别为3.34、3.33和10.41。还将该方法与两种众所周知的胸肌检测技术进行了比较，并且在大多数情况下，它的性能优于其他两种方法。

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