Computer-Aided Lung Parenchyma Segmentation Using Supervised Learning

2019 ◽  
pp. 403-412
Author(s):  
G. N. Balaji ◽  
P. Subramanian
Keyword(s):  
Supervised Learning ◽  
Lung Parenchyma ◽  
Computer Aided

scholarly journals Supervised Learning Approach to Parametric Computer-Aided Design Geometry Repair

AIAA Journal ◽  
2006 ◽  
Vol 44 (2) ◽  
pp. 282-289 ◽  
Author(s):  
András Sóbester ◽  
Andy J. Keane
Keyword(s):  
Supervised Learning ◽  
Computer Aided Design ◽  
Learning Approach ◽  
Computer Aided ◽  
Aided Design

scholarly journals COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT)

2020 ◽  
Author(s):  
Roberto Grassi ◽  
Maria Paola Belfiore ◽  
Alessandro Montanelli ◽  
Gianluigi Patelli ◽  
Fabrizio Urraro ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Parenchyma ◽  
Ground Glass ◽  
Chest Computed Tomography ◽  
Computer Aided ◽  
Healthy Lung

scholarly journals A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

2012 ◽  
Vol 26 (3) ◽  
pp. 496-509 ◽  
Author(s):  
Shiloah Elizabeth Darmanayagam ◽  
Khanna Nehemiah Harichandran ◽  
Sunil Retmin Raj Cyril ◽  
Kannan Arputharaj
Keyword(s):  
Lung Parenchyma ◽  
Computer Aided Diagnosis ◽  
Chest Ct ◽  
Computer Aided ◽  
Aided Diagnosis

Pulmonary nodule detection using chest CT images

2003 ◽  
Vol 44 (3) ◽  
pp. 252-257 ◽  
Author(s):  
D.-Y. Kim ◽  
J.-H. Kim ◽  
S.-M. Noh ◽  
J.-W. Park
Keyword(s):  
Pulmonary Nodule ◽  
Lung Parenchyma ◽  
Computer Aided Diagnosis ◽  
Detection Sensitivity ◽  
Thoracic Wall ◽  
Volume Calculation ◽  
Nodule Detection ◽  
Computer Aided ◽  
Pulmonary Nodule Detection ◽  
Aided Diagnosis

Purpose: Automated methods for the detection of pulmonary nodules and nodule volume calculation on CT are described. Material and Methods: Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchyma from the thoracic wall and mediastinum. A deformable model was applied to segment the lung boundaries, and the segmentation results were compared with the thresholding method. The lesions that had high gray values were extracted from the segmented lung parenchyma. The selected lesions included nodules, blood vessels and partial volume effects. The discriminating features such as size, solid shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudolesions. With texture features of true nodules, the contour-following method, which tracks the segmented lung boundaries, was applied to detect juxtapleural nodules that were contiguous to the pleural surface. Volume and circularity calculations were performed for each identified nodule. The identified nodules were sorted in descending order of volume. These methods were applied to 827 image slices of 24 cases. Results: Computer-aided diagnosis gave a nodule detection sensitivity of 96% and no false-positive findings. Conclusion: The computer-aided diagnosis scheme was useful for pulmonary nodule detection and gave characteristics of detected nodules.

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