基于动态自适应体素生长的肺部CT图像3维分割算法

A Dynamic Adaptive 3D Voxel-growing Segmentation Algorithm for Pulmonary CT Images

为了解决传统体素生长算法中容易产生分割泄漏问题以及左右肺分离的难题,提出了一种动态自适应的体素生长算法,用于对肺部CT图像进行3维分割。该算法采用了半侧肺预分割的方式,并针对分割区域的局部统计信息动态调整分割参数,有效地避免了分割泄漏,并在准确度和鲁棒性方面得到了很大的改进,而且能够应用于纯3维环境的自动分割。该算法通过对多组实验数据,包括健康人和不同类型肺病患者的肺部CT图像进行试验,取得了良好的效果,为肺部组织的定量计算以及临床应用提供了可靠的基础。

A novel algorithm based on dynamic adaptive voxel-growing method is proposed to segment 3D CT pulmonary volumes. It selects the optimal parameters for segmentation by dynamically adjusting statistical information about previous segmented regions. To avoid the disadvantage of leaking during segmentation with the conventional voxel-growing based methods, it adopts a process to mutually utilize segment results between both of lateral lung leaves, which in turn benefit the discriminative segmentation on left and right lung leaves. Compared with conventional algorithms, the accuracy and robustness of segmentation are enhanced, and still this algorithm has the advantage of pure 3D processing ability. Groups of experimental data is verified with this algorithm, including data about the healthy people and types of patients with lung diseases. Results are effective, which imply that this algorithm is potentially valid for future clinical diagnosis applications.