Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features.
Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features.
复制标题
DOI：
10.3390/jimaging6020006
复制
发表时间：
2020-02-24
影响因子：
3.2
通讯作者：
Khan MH
中科院分区：
文献类型：
--
作者：
Khehrah N;Farid MS;Bilal S;Khan MH
肺癌是危害最大的恶性肿瘤之一。它的发生率和死亡率都很高，因为它经常在后期被诊断出来。计算机断层扫描(CT)被广泛用于区分疾病；计算机辅助系统正在被创建，以有效地分析疾病的早期阶段。在本文中，我们提出了一种从肺部CT图像中检测结节的全自动框架。计算灰度CT图像的直方图以自动将肺部区域从基础中分离出来。使用形态运算符对结果进行改进。然后从薄壁组织中提取内部结构。提出了一种基于阈值的技术来将候选结节与其他结构(如细支气管和血管)分开。为这些候选结节提取不同的统计特征和基于形状的特征，形成结节特征向量，并使用支持向量机对其进行分类。在从肺图像数据库联盟(LIDC)收集的大型肺部CT数据集上对所提出的方法进行了评估。与现有同类方法相比，该方法取得了很好的效果，敏感度达到93.75%，证明了该方法的有效性。
The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.