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Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

Ozekes S, Osman O, Ucan ON - Korean J Radiol (2008 Jan-Feb)

Bottom Line: First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN).Finally, fuzzy rule based thresholding was applied and the ROI's were found.Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

View Article: PubMed Central - PubMed

Affiliation: Istanbul Commerce University, Ragip Gumuspala Cad. No: 84 34378 Eminonu, Istanbul, Turkey. serhat@iticu.edu.tr

ABSTRACT

Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels.

Materials and methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset.

Results: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm.

Conclusion: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

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A. Second CT image, B. Segmented lung region using cellular neural network, C. CT image in the lung region, D. Voxels having suitable density values, E. ROIs in the lung region, F. Detected nodule region.
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Figure 3: A. Second CT image, B. Segmented lung region using cellular neural network, C. CT image in the lung region, D. Voxels having suitable density values, E. ROIs in the lung region, F. Detected nodule region.

Mentions: In order to segment the lung region, 10 neighborhoods were used for the CNN templates A and B, which represent the feedback and feed-forward connections, respectively. Another CNN template I was used as an offset matrix. 66 elements were needed for the A and B templates to have a symmetrical form. Thus, element vector S included 133 elements: 66 elements for A, 66 elements for B and 1 element for I. In Figures 2A, 3A and 4A, three serial CT images are shown and the segmented ung images with using CNN are given in Figures 2B, 3B and 4B. Figures 2C, 3C and 4C show the CT image in the lung region.


Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

Ozekes S, Osman O, Ucan ON - Korean J Radiol (2008 Jan-Feb)

A. Second CT image, B. Segmented lung region using cellular neural network, C. CT image in the lung region, D. Voxels having suitable density values, E. ROIs in the lung region, F. Detected nodule region.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2627180&req=5

Figure 3: A. Second CT image, B. Segmented lung region using cellular neural network, C. CT image in the lung region, D. Voxels having suitable density values, E. ROIs in the lung region, F. Detected nodule region.
Mentions: In order to segment the lung region, 10 neighborhoods were used for the CNN templates A and B, which represent the feedback and feed-forward connections, respectively. Another CNN template I was used as an offset matrix. 66 elements were needed for the A and B templates to have a symmetrical form. Thus, element vector S included 133 elements: 66 elements for A, 66 elements for B and 1 element for I. In Figures 2A, 3A and 4A, three serial CT images are shown and the segmented ung images with using CNN are given in Figures 2B, 3B and 4B. Figures 2C, 3C and 4C show the CT image in the lung region.

Bottom Line: First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN).Finally, fuzzy rule based thresholding was applied and the ROI's were found.Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

View Article: PubMed Central - PubMed

Affiliation: Istanbul Commerce University, Ragip Gumuspala Cad. No: 84 34378 Eminonu, Istanbul, Turkey. serhat@iticu.edu.tr

ABSTRACT

Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels.

Materials and methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset.

Results: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm.

Conclusion: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

Show MeSH
Related in: MedlinePlus