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. 'is paper develops a novel automatic segmentation model...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/29/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. 'is paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. 'e 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. 'en, 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). 'e 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. 'e training performance shows the effectiveness for a combination of texture and deep radiomics features in lung segmentation. |
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