Research Article
Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease
Algorithm 1
Training for lung ROI selection for HRCT image.
| Input: annotated images. | (1) | Step 1: preprocessing. | (2) | 1.1: utilization of Wiener filter for removing the Gaussian noise of the lung area. | (3) | Step 2: landmark detection. | (4) | 2.1: hand-crafted feature selection using GLCM mining the texture features. | (5) | 2.2: deep feature selection using U-Net including convolution and pooling. | (6) | 2.3: feature fusion combining the deep and texture features. | (7) | Step 3: ROI selection. | (8) | 3.1: define the position of ROI based on the features. | (9) | if the ROI has the same edge with the previous training then | (10) | Segment the ROI. | | Output: the learned lung ROI mask. |
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