Lee Youn Joo, Lee Minho, Kim Namkug, Seo Joon Beom, Park Joo Young
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
J Digit Imaging. 2014 Aug;27(4):538-47. doi: 10.1007/s10278-014-9680-5.
This study presents a completely automated method for separating the left and right lungs using free-formed surface fitting on volumetric computed tomography (CT). The left and right lungs are roughly divided using iterative 3-dimensional morphological operator and a Hessian matrix analysis. A point set traversing between the initial left and right lungs is then detected with a Euclidean distance transform to determine the optimal separating surface, which is then modeled from the point set using a free-formed surface-fitting algorithm. Subsequently, the left and right lung volumes are smoothly and directly separated using the separating surface. The performance of the proposed method was estimated by comparison with that of a human expert on 44 CT examinations. For all data sets, averages of the root mean square surface distance, maximum surface distance, and volumetric overlap error between the results of the automatic and the manual methods were 0.032 mm, 2.418 mm, and 0.017 %, respectively. Our study showed the feasibility of automatically separating the left and right lungs by identifying the 3D continuous separating surface on volumetric chest CT images.
本研究提出了一种在容积计算机断层扫描（CT）上使用自由曲面拟合来分离左右肺的完全自动化方法。利用迭代三维形态学算子和黑塞矩阵分析对左右肺进行粗略划分。然后通过欧几里得距离变换检测初始左右肺之间的点集遍历，以确定最佳分离表面，接着使用自由曲面拟合算法从该点集对其进行建模。随后，使用分离表面将左右肺体积平滑且直接地分离。通过与人类专家对44例CT检查结果进行比较，评估了所提方法的性能。对于所有数据集，自动方法和手动方法结果之间的均方根表面距离、最大表面距离和体积重叠误差的平均值分别为0.032毫米、2.418毫米和0.017%。我们的研究表明，通过识别容积胸部CT图像上的三维连续分离表面来自动分离左右肺是可行的。
