基于RandomWalk算法的CT图像肺实质自动分割

针对复杂情况下肺实质的分割问题,提出了一种基于Random Walk算法对肺实质自动分割的方法。首先,根据胸部组织解剖学及其计算机断层扫描(CT)图像的影像学特征,在肺实质及其周围组织分别确定目标区域种子点和背景种子点位置;然后,使用Random Walk算法对CT图像进行分割,提取近似肺区域的掩模;接下来,对掩模实施数学形态学运算,来进一步调整目标区域种子点和背景种子点的标定位置,使其适合具体的复杂情况;最后,再次使用Random Walk算法分割图像,得到最终的肺实质分割结果。实验结果显示,该方法与金标准的平均绝对距离为0.44±0.13 mm,重合率(DC)为99.21%±0.38%。与其他分割方法相比,该方法在分割精度上得到了显著提高。结果表明,提出的方法能够解决复杂情况下肺实质分割的问题,确保了分割的完整性、准确性、实时性和鲁棒性,分割结果和时间均可满足临床需求。

Automated lung segmentation for chest CT images based on Random Walk algorithm

To deal with the lung segmentation problem under complex conditions, Random Walk algorithm was applied to automatic lung segmentation. Firstly, according to the anatomical and imaging characteristics of the chest Computed Tomography (CT) images, foreground and background seeds were selected respectively. Then, CT image was segmented roughly by using the Random Walk algorithm and the approximate mask of lung area was extracted. Next, through implementing mathematical morphology operations to the mask, foreground and background seeds were further adjusted to adapt to the actually complicated situations. Finally, the fine segmentation of lung parenchyma for chest CT image was implemented by using the Random Walk algorithm again. The experimental results demonstrate that, compared with the gold standard, the Mean Absolute Distance (MAD) is 0.44±0.13 mm, the Dice Coefficient (DC) is 99.21%±0.38%. Compared with the other lung segmentation methods, the proposed method are significantly improved in accuracy of segmentation. The experimental results show that the proposed method can solve the difficult cases of the lung segmentation, and ensure the integrity, accuracy, real-time and robustness of the segmentation. Meanwhile, the results and time of the proposed method can meet the clinical needs.