![](/images/graphics-bg.png)
Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease
Joint Authors
Pang, Ting
Guo, Shaoyong
Zhang, Xinwang
Zhao, Lijie
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Pang, Ting& Guo, Shaoyong& Zhang, Xinwang& Zhao, Lijie. 2019. Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease. BioMed Research International،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1123634
Modern Language Association (MLA)
Pang, Ting…[et al.]. Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease. BioMed Research International No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1123634
American Medical Association (AMA)
Pang, Ting& Guo, Shaoyong& Zhang, Xinwang& Zhao, Lijie. Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1123634
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1123634