Institute of EduInfo Science & Engineering, Nanjing Normal University, Jiangsu, China.
Department of Information Science and Engineering, Zaozhuang University, Shandong, China.
Med Phys. 2020 Feb;47(2):518-529. doi: 10.1002/mp.13939. Epub 2019 Dec 29.
Several negative factors, such as juxta-pleural nodules, pulmonary vessels, and image noise, make accurately segmenting lungs from computed tomography (CT) images a complex task. We propose a novel hybrid automated algorithm in the paper based on random forest to deal with the issues. Our method aims to eliminate the effect of the factors and generate accurate segmentation of lungs from CT images.
Our algorithm consists of five main steps: image preprocessing, lung region extraction, trachea elimination, lung separation, and contour correction. A lung CT image is first preprocessed with a novel normal vector correlation-based image denoising approach and decomposed into a group of multiscale subimages. A modified superpixel segmentation method is then performed on the first-level subimage to generate a set of superpixels, and a random forest classifier is employed to segment the lungs by classifying the superpixels of each subimage-based on the features extracted from them. The initial lung segmentation result is further refined through trachea elimination using an iterative thresholding approach, lung separation based on context information of image sequence, and contour correction with a corner detection technique.
Our algorithm is tested on a set of CT images affected with interstitial lung diseases, and experiments show that the algorithm achieves high accuracy on lung segmentation with 0.9638 Jaccard's index and 0.9867 Dice similarity coefficient, compared with ground truths. Additionally, our algorithm achieves an average 7.7% better Dice similarity coefficient than compared conventional lung segmentation methods and 1% better than Deep Learning.
Our algorithm can segment lungs from lung CT images with good performance in a fully automatic fashion, and it is of great assistance for lung disease detection in the computer-aided detection system.
近胸膜结节、肺血管和图像噪声等多种负性因素使得从计算机断层扫描（CT）图像中准确分割肺成为一项复杂的任务。我们提出了一种新的基于随机森林的混合自动算法来解决这个问题。我们的方法旨在消除这些因素的影响，并从 CT 图像中生成准确的肺分割。
我们的算法由五个主要步骤组成：图像预处理、肺区提取、气管消除、肺分离和轮廓校正。首先，用一种新的基于法向量相关的图像去噪方法对肺 CT 图像进行预处理，并将其分解成一组多尺度子图像。然后，对第一层子图像进行改进的超像素分割方法，生成一组超像素，并使用随机森林分类器通过基于从超像素中提取的特征对每个子图像的超像素进行分类，从而分割肺。通过迭代阈值处理、基于图像序列上下文信息的肺分离以及角点检测技术的轮廓校正，进一步细化初始肺分割结果。
我们的算法在一组受间质性肺病影响的 CT 图像上进行了测试，实验表明，该算法在肺分割方面具有很高的准确性，Jaccard 指数为 0.9638，Dice 相似系数为 0.9867，与真实值相比。此外，与传统的肺分割方法相比，我们的算法平均提高了 7.7%的 Dice 相似系数，比深度学习提高了 1%。
我们的算法可以以全自动的方式从肺 CT 图像中分割出肺，这对计算机辅助检测系统中的肺病检测有很大帮助。
