摘要
本文主要研究肺部CT图像中胸膜表面以及近胸膜肺结节的分割方法.由于胸膜粘附结点与肺实质周围的灰度值类似,因此通过边界跟踪获得的轮廓很可能会被过度分割.针对这个不足,本文提出了一种基于Graham扫描法和Harris角点检测算法的分割方法.该方法首先运用最大类间方差法将原CT图像转化为二值图像,并初步提取出肺实质部分的轮廓.然后运用凸包与凸缺陷以及角点检测方法对边界进行校正,从而得到完整的模板.最后根据校正后的模板分割出肺实质内部的所有结节候选点.本文对TCIA(The Cancer Imaging Archive)数据库中的263张CT样本进行实验并将实验结果与滚球算法、水平集方法以及边界逼近法得到的实验结果作对比.最后分析对比结果并证明本方法的有效性.
This paper discusses how to segment the juxtapleural nodule and the nodule closed to the pleura. As the nodule which adjoins pleura is the same as the region with pulmonary parenchyma in gray level. The contour gained by the method of tracking contour are likely to be over-segmented. For this,we employed a method based on the Graham scan and harris corner detection algorithm. First,we transformed the original CT image into a binary image by means of the maximum class inter-variance method and extracted the outline of the lung parenchyma. Then we used the Graham scan and harris corner detection algorithm to correct the boundary and got a complete template. Finally,all nodular candidates within the lung parenchyma were identified through the corrected template. In this paper,we chose 263 sample images in The Cancer Imaging Archive( TCIA) to implement the experiment and compared the result with the rolling ball algorithm,level set method and boundary approximation. At last,we analyse the result and prove the effectiveness of the method.
引文
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[1]黄智定,孙红.基于边界逼近的肺实质分割方法[J].中国生物医学工程学报,2016,35(5):513-518.