Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease

Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). Traditional methods are less intelligent and have lower accuracy of segmentation. This paper develops a novel automatic segmentation model...

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Bibliographic Details
Published in:BioMed research international 2019, Vol.2019 (2019), p.1-8
Main Authors: Zhao, Lijie, Zhang, Xinwang, Guo, Shaoyong, Pang, Ting
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial neural networks
Computed tomography
CT imaging
Databases, Factual
Deep learning
Diagnosis, Computer-Assisted - methods
Diagnostic imaging
Feature extraction
Female
Fuzzy sets
Humans
Image processing
Image resolution
Image segmentation
International conferences
Lung - diagnostic imaging
Lung - pathology
Lung diseases
Lung Diseases, Interstitial - diagnostic imaging
Male
Mammography
Medical imaging
Medical imaging equipment
Methods
Middle Aged
Models, Theoretical
Neural networks
Neural Networks, Computer
Noise
Noise reduction
Pattern recognition
Radiomics
Signal processing
Texture
Tomography, X-Ray Computed - methods
Training
Wavelet transforms
Wiener filtering
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Summary:Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). Traditional methods are less intelligent and have lower accuracy of segmentation. This paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. The study uses ILD Database-MedGIFT from 128 patients with 108 annotated image series and selects 1946 regions of interest (ROI) of lung tissue patterns for training and testing. First, images are denoised by Wiener filter. Then, segmentation is performed by fusion of features that are extracted from the gray-level co-occurrence matrix (GLCM) which is a classic texture analysis method and U-Net which is a standard convolutional neural network (CNN). The final experiment result for segmentation in terms of dice similarity coefficient (DSC) is 89.42%, which is comparable to the state-of-the-art methods. The training performance shows the effectiveness for a combination of texture and deep radiomics features in lung segmentation.
ISSN:2314-6133
2314-6141
DOI:10.1155/2019/2045432