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A region based active contour method for x-ray lung segmentation using prior shape and low level features

机译:基于区域的主动轮廓线方法,用于使用先前形状和低水平特征进行X线肺部分割

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In this work, a level set energy for segmenting the lungs from digital Posterior-Anterior (PA) chest x-ray images is presented. The primary challenge in using active contours for lung segmentation is local minima due to shading effects and presence of strong edges due to the rib cage and clavicle. We have used the availability of good contrast at the lung boundaries to extract a multi-scale set of edge/corner feature points and drive our active contour model using these features. We found these features when supplemented with a simple region based data term and a shape term based on the average lung shape, able to handle the above local minima issues. The algorithm was tested on 1130 clinical images, giving promising results.
机译:在这项工作中,提出了一种用于从数字化后后(PA)胸部X射线图像中分割肺部的水平设定能量。使用主动轮廓进行肺分割的主要挑战是由于阴影效应以及由于肋骨和锁骨而导致的强边缘的存在,从而导致局部极小。我们使用了在肺边界处具有良好对比度的可用性来提取多尺度的边缘/角特征点集,并使用这些特征来驱动我们的活动轮廓模型。我们发现这些特征在补充了基于简单区域的数据项和基于平均肺部形状的形状项时可以处理上述局部极小问题。该算法在1130张临床图像上进行了测试,结果令人鼓舞。

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