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. 2008 Jan-Feb;9(1):1-9.
doi: 10.3348/kjr.2008.9.1.1.

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

Serhat Ozekes  1 , Onur Osman, Osman N Ucan
Affiliations

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

Serhat Ozekes et al. Korean J Radiol. 2008 Jan-Feb.

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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Figures

Fig. 1
Fig. 1
Procedural flowchart for detecting lung nodules.
Fig. 2
Fig. 2
A. First 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.
Fig. 3
Fig. 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.
Fig. 4
Fig. 4
A. Third 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.
Fig. 5
Fig. 5
Procedural flowchart of ROI specification.
Fig. 6
Fig. 6
A. A voxel which doesn't have a number of adjacent neighbor voxels greater than or equal to the value of "minimum distance threshold", so it is not a part of the ROI. B. A voxel which doesn't have a number of adjacent neighbor voxels less than or equal to the value of "maximum distance threshold", so it is not a part of the ROI. C. A voxel which has a number of adjacent neighbor voxels greater than or equal to the value of "minimum distance threshold", and less than or equal to the value of "maximum distance threshold", so it is a part of the ROI.
Fig. 7
Fig. 7
Threshold membership functions
Fig. 8
Fig. 8
A. Free-response receiver operating characteristic curve showing the performance of the nodule detection task according to the numbers of cases. B. Minimum nodule thickness versus false-positive regions per case.
See this image and copyright information in PMC

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