Lungs nodule detection framework from computed tomography images using support vector machine
Lungs nodule detection framework from computed tomography images using support vector machine
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DOI：
10.1002/jemt.23275
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发表时间：
2019-08-01
影响因子：
2.5
通讯作者：
Awais, Muhammad
中科院分区：
文献类型：
--
作者：
Khan, Sajid A.;Nazir, Muhammad;Awais, Muhammad
云基础设施的出现有可能为医学成像领域的各个领域带来显着的好处。广泛使用云基础设施进行医学图像处理背后的驱动力是计算机断层扫描 (CT) 和磁共振成像 (MRI) 数据规模的指数级增长。自这些成像技术问世以来，单张 CT/MRI 图像的大小已成倍增加。这就需要引入有效且高效的框架来从这些大型图像中提取相关且最合适的信息（特征）。由于早期发现肺癌可以显着增加肺部扫描患者的生存机会，因此有效且高效的结节检测系统可以发挥至关重要的作用。在本文中，我们提出了一种新颖的肺结节分类框架，该框架具有较低的误报率（FPR）、较高的准确率、较高的灵敏度、较低的计算成本，并且使用少量特征，同时保留边缘和纹理信息。所提出的框架包括多个阶段，包括图像对比度增强、分割、特征提取，然后利用这些特征来训练和测试选定的分类器。图像预处理和特征选择是主要步骤，在提高分类精度方面发挥着至关重要的作用。我们利用著名的肺部图像联盟数据库数据集对我们技术的有效性进行了实证测试。结果证明，该技术对于降低 FPR 非常有效，敏感性高达 97.45%。
The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps-playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well-known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.