This work presents a high-performance approach for 3D lung segmentation tasks in computer tomography images using a new intelligent machine learning algorithm called Floor of Log(FoL). The Support Vector Machine was used to learn the better parameter of the FoL algorithm using the parenchyma and its border as labels. Sensitivity, Matthews Correlation Coefficient (MCC), Hausdorff Distance (HD), Dice, Accuracy (ACC), and Jaccard were used to evaluate the proposed algorithm. The FoL was compared with recent 3D region growing, 3D Adaptive Crisp Active Contour, 3D OsiriX toolbox, and Level-set algorithm based on the coherent propagation method algorithms. The FoL algorithm achieves good results with approximately 19 s in the most significant result in an exam with 430 slices and presents similarity indexes achieving HD 3.5, DICE 83.63, and Jaccard 99.73 and qualitative indexes achieving Sensitivity 83.87, MCC 83.08, and ACC 99.62. The proposed approach of this work showed a simple and powerful algorithm to segment lung in computer tomography images of the chest region by combining similar textures, highlighting the lung structure. The FoL was presented as a new supervised clustering algorithm which can be trained to achieve better results and attached to other approaches as Convolutional Deep Neural Networks applications.

中文翻译：

---

Floor of log：一种用于计算机断层扫描图像中 3D 肺分割的新型智能算法

这项工作使用一种名为 Floor of Log (FoL) 的新型智能机器学习算法，为计算机断层扫描图像中的 3D 肺分割任务提供了一种高性能方法。支持向量机用于学习使用薄壁组织及其边界作为标签的 FoL 算法的更好参数。灵敏度、马修斯相关系数 (MCC)、豪斯多夫距离 (HD)、骰子、精度 (ACC) 和 Jaccard 用于评估所提出的算法。FoL 与最近的 3D 区域增长、3D Adaptive Crisp Active Contour、3D OsiriX 工具箱和基于相干传播方法算法的 Level-set 算法进行了比较。FoL 算法在具有 430 个切片的检查中以大约 19 秒的最显着结果获得了良好的结果，并提供了达到 HD 3.5、DICE 83.63 和 Jaccard 99 的相似性指数。73 和定性指标达到灵敏度 83.87、MCC 83.08 和 ACC 99.62。这项工作提出的方法展示了一种简单而强大的算法，通过组合相似的纹理，突出肺结构，在胸部区域的计算机断层扫描图像中分割肺。FoL 是一种新的监督聚类算法，可以通过训练获得更好的结果，并附加到其他方法作为卷积深度神经网络应用程序。