An automatic pulmonary nodules detection algorithm with multi-scale information fusion.
10.7507/1001-5515.201910047
- Author:
Xiuling LIU
1
,
2
;
Shuaishuai QI
3
;
Peng XIONG
1
,
2
;
Jing LIU
4
;
Hongrui WANG
1
,
2
;
Jianli YANG
1
,
2
Author Information
1. College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
2. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China.
3. College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
4. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, P.R.China.
- Publication Type:Journal Article
- Keywords:
computed tomography images;
feature fusion;
multi scale;
pulmonary nodule detection
- From:
Journal of Biomedical Engineering
2020;37(3):434-441
- CountryChina
- Language:Chinese
-
Abstract:
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
