Ziyad Shabana Rasheed, Radha Venkatachalam, Vayyapuri Thavavel
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin AbdulAziz University, Al Kharj, Saudi Arabia.
Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
Curr Med Imaging Rev. 2020;16(1):16-26. doi: 10.2174/1573405615666190206153321.
Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules.
The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx.
This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules.
A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper.
The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.
肺癌已成为癌症相关死亡的主要原因。检测潜在恶性肺结节对于肺癌的早期诊断和临床管理至关重要。在临床实践中，由于病例数量众多，放射科医生对计算机断层扫描（CT）图像的解读具有挑战性。人工检查结果中假阳性率很高。计算机辅助检测系统（CAD）和计算机辅助诊断系统（CADx）可帮助放射科医生更准确地识别肺结节。
目的是分析用于肺结节检测的CAD和CADx系统。有必要回顾不同研究人员提出并实施的CAD和CADx系统中采用的各种技术。本研究旨在分析计算机科学中各种概念在CAD和CADx各阶段的最新应用。
这篇综述文章独具特色，因为它分析了不同知名研究人员在噪声去除、对比度增强、胸廓去除、肺分割、骨抑制、气管分割、结节与非结节分类以及良性和恶性结节最终分类等方面提出的各种技术。
本文已将不同研究人员实施的不同技术在结节与非结节分类方面的性能比较制成表格。
这篇综述文章的研究结果肯定会对致力于肺结节检测自动化的研究群体有用。
