An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy.
An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy.
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DOI：
10.1016/j.compbiomed.2014.12.008
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发表时间：
2015-03
影响因子：
7.7
通讯作者：
Hsu W
中科院分区：
文献类型：
--
作者：
Shen S;Bui AA;Cong J;Hsu W
计算机辅助检测和诊断（CAD）已被广泛研究，以提高放射科医生在检测和表征肺部疾病方面的诊断准确性，以及协助处理越来越大的成像体积。肺分割是大多数CAD方案的必要预处理步骤。本文提出了一种无参数的肺分割算法，旨在提高肺结节检测的准确性，侧重于胸膜旁结节。一个双向链编码方法结合支持向量机（SVM）分类器被用来选择性地平滑肺部边界，同时最大限度地减少相邻区域的过度分割。该自动化方法在来自肺成像数据库联盟（LIDC）的233项计算机断层扫描（CT）研究中进行了测试，代表了403个胸膜旁结节。该方法获得了92.6%的再纳入率。在10个随机选择的CT系列上进一步验证了分割准确性，与专家进行的手动分割参考标准相比，平均过度分割率为0.3%，分割率为2.4%。
Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists’ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert.
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