False positive reduction in pulmonary nodule classification using 3D texture and edge feature in CT images

被引:7
作者
Wang, Bin [1 ]
Si, Shuaizong [1 ]
Zhao, Hai [1 ]
Zhu, Hongbo [1 ]
Dou, Shengchang [1 ]
机构
[1] Northeastern Univ, Sch Comp Sci & Engn, 195 Chuangxin Rd Hunnan Dist, Shenyang 110169, Liaoning, Peoples R China
关键词
3D pulmonary nodule detection; small pulmonary nodule reconstruction; local binary patterns (LBP); edge orientation histogram (EOH); H-SVM classifier; CT image database; LUNG NODULES; CHEST;
D O I
10.3233/THC-181565
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
BACKGROUND: Pulmonary nodule detection can significantly influence the early diagnosis of lung cancer while is confused by false positives. OBJECTIVE: In this study, we focus on the false positive reduction and present a method for accurate and rapid detection of pulmonary nodule from suspective regions with 3D texture and edge feature. METHODS: This work mainly consists of four modules. Firstly, small pulmonary nodule candidates are preprocessed by a reconstruction approach for enhancing 3D image feature. Secondly, a texture feature descriptor is proposed, named cross-scale local binary patterns (CS-LBP), to extract spatial texture information. Thirdly, we design a 3D edge feature descriptor named orthogonal edge orientation histogram (ORT-EOH) to obtain spatial edge information. Finally, hierarchical support vector machines (H-SVMs) is used to classify suspective regions as either nodules or non-nodules with joint CS-LBP and ORT-EOH feature vector. RESULTS: For the solitary solid nodule, ground-glass opacity, juxta-vascular nodule and juxta-pleural nodule, average sensitivity, average specificity and average accuracy of our method are 95.69%, 96.95% and 96.04%, respectively. The elapsed time in training and testing stage are 321.76 s and 5.69 s. CONCLUSIONS: Our proposed method has the best performance compared with other state-of-the-art methods and is shown the improved precision of pulmonary nodule detection with computationaly low cost.
引用
收藏
页码:1071 / 1088
页数:18
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