An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network.
10.7507/1001-5515.201908060
- Author:
Huahai ZHANG
1
;
Peirui BAI
1
;
Ziyang GUO
1
;
Linghao DU
1
;
Chang LI
1
;
Yande REN
2
;
Kai YANG
1
;
Qingyi LIU
1
Author Information
1. College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P.R.China.
2. Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 265000, P.R.China.
- Publication Type:Journal Article
- Keywords:
pulmonary parenchymal segmentation;
pulse coupled neural network;
surfacelet transform;
three-dimensional medical image segmentation
- MeSH:
Algorithms;
Neural Networks, Computer;
Tomography, X-Ray Computed
- From:
Journal of Biomedical Engineering
2020;37(4):630-640
- CountryChina
- Language:Chinese
-
Abstract:
In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacelet transform and pulse coupled neural network (PCNN). First, the three-dimensional computed tomography of lungs is decomposed into surfacelet transform domain to obtain multi-scale and multi-directional sub-band information. The edge features are then enhanced by filtering sub-band coefficients using local modified Laplacian operator. Second, surfacelet inverse transform is implemented and the reconstructed image is fed back to the input of PCNN. Finally, iteration process of the PCNN is carried out to obtain final segmentation result. The proposed algorithm is validated on the samples of public dataset. The experimental results demonstrate that the proposed algorithm has superior performance over that of the three-dimensional surfacelet transform edge detection algorithm, the three-dimensional region growing algorithm, and the three-dimensional U-NET algorithm. It can effectively suppress the interference coming from lung lesions and bronchial, and obtain a complete structure of lung parenchyma.
