Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.
Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.
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DOI：
10.1109/jbhi.2014.2328870
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发表时间：
2015-03
影响因子：
7.7
通讯作者：
Liang Z
中科院分区：
文献类型：
--
作者：
Han H;Li L;Han F;Song B;Moore W;Liang Z
肺结节的计算机辅助检测（CADe）对于帮助放射科医生从计算机断层扫描（CT）中早期识别肺癌至关重要。本文提出了一种基于层次矢量量化（VQ）方案的新型CADe系统。与常用的简单阈值法相比，高VQ可以更准确地从胸容积中分割肺。在确定肺内的初始候选结节（INCs）时，低水平VQ被证明对INCs检测和分割是有效的，与现有方法相比，计算效率也很高。通过基于规则的过滤操作结合基于特征的支持向量机分类器进行假阳性（FP）降低。该系统在公开在线LIDC（肺图像数据库联盟）数据库的205例患者中进行了验证，每个病例至少有一个胸膜旁结节注释。实验结果表明，我们的CADe系统在4 FPs/次扫描的特异性下获得了82.7%的总灵敏度，在4.14 FPs/次扫描的特异性下获得了89.2%的敏感性。相对于类似的CADe系统，该系统表现出了优异的性能，并证明了其通过CT成像快速和自适应检测肺结节的潜力。
Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier. The proposed system was validated on 205 patient cases from the publically available on-line LIDC (Lung Image Database Consortium) database, with each case having at least one juxta-pleural nodule annotation. Experimental results demonstrated that our CADe system obtained an overall sensitivity of 82.7% at a specificity of 4 FPs/scan, and 89.2% sensitivity at 4.14 FPs/scan for the classification of juxta-pleural INCs only. With respect to comparable CADe systems, the proposed system shows outperformance and demonstrates its potential for fast and adaptive detection of pulmonary nodules via CT imaging.