Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans
Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first ste...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2013/517632 |
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| author | Ayman El-Baz Ahmed Elnakib Mohamed Abou El-Ghar Georgy Gimel'farb Robert Falk Aly Farag |
| author_facet | Ayman El-Baz Ahmed Elnakib Mohamed Abou El-Ghar Georgy Gimel'farb Robert Falk Aly Farag |
| author_sort | Ayman El-Baz |
| collection | DOAJ |
| description | Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts. |
| format | Article |
| id | doaj-art-a160c4efc2434757a660e31d118e2a29 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a160c4efc2434757a660e31d118e2a292025-02-03T05:45:36ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962013-01-01201310.1155/2013/517632517632Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT ScansAyman El-Baz0Ahmed Elnakib1Mohamed Abou El-Ghar2Georgy Gimel'farb3Robert Falk4Aly Farag5Bioimaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USABioimaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USAUrology and Nephrology Department, University of Mansoura, Mansoura 35516, EgyptDepartment of Computer Science, The University of Auckland 1142, Auckland, New ZealandMedical Imaging Division, Jewish Hospital, Louisville, KY 40202, USAElectrical and Computer Engineering Department, University of Louisville, KY 40292, USAAutomatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.http://dx.doi.org/10.1155/2013/517632 |
| spellingShingle | Ayman El-Baz Ahmed Elnakib Mohamed Abou El-Ghar Georgy Gimel'farb Robert Falk Aly Farag Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans International Journal of Biomedical Imaging |
| title | Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans |
| title_full | Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans |
| title_fullStr | Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans |
| title_full_unstemmed | Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans |
| title_short | Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans |
| title_sort | automatic detection of 2d and 3d lung nodules in chest spiral ct scans |
| url | http://dx.doi.org/10.1155/2013/517632 |
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