Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system
Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system
复制标题
DOI：
10.1016/j.compbiomed.2012.12.004
复制
发表时间：
2013-05-01
影响因子：
7.7
通讯作者：
Boostani, Reza
中科院分区：
文献类型：
--
作者：
Keshani, Mohsen;Azimifar, Zohreh;Boostani, Reza
提出了一种基于CT图像的肺结节检测、分割和识别的新方法。我们的贡献包括几个步骤。首先，肺区域被分割的活动轮廓建模，然后通过一些掩蔽技术，将非孤立的结节到孤立的。然后，结节检测支持向量机（SVM）分类器使用有效的二维随机和三维解剖特征。然后通过主动轮廓建模提取检测到的结节的轮廓。在此步骤中，所有实性和空洞结节都被准确分割。最后，将肺组织分为四类：即肺壁、实质、细支气管和结节。这种分类有助于我们区分与肺壁和/或细支气管相连的结节（附着结节）和被实质覆盖的结节（孤立结节）。最后，我们提出的方法的性能进行了检查，并通过实验与其他有效的方法使用临床CT图像和两组公共数据集从肺图像数据库联盟（LIDC）和ANODE 09。实性、非实性和空洞性结节均被检出，总体检出率为89%;假阳性数为7.3/次扫描，所有检出结节的位置均被正确识别。(c)2012爱思唯尔有限公司保留所有权利。
In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%; the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly. (c) 2012 Elsevier Ltd. All rights reserved.