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. 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. 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. Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computeraided detection of lung nodules. he number of cases of lung cancer is increasing year by year, so screening for this malady has become popular in advanced countries (1). The detection of lung cancer at an early stage is very important to cure it; however, it is very difficult for radiologists to detect and to diagnose lung cancer on chest x-ray images because of following reasons. There are many tissues that overlap each other on chest radiographs. The presence of cancerous tumors is obscured by the overlying ribs, bronchia, blood vessels and other normal anatomic structures. The shadows of cancerous tumors seen on chest radiograph are usually vague and subtle, and they tend to be missed. The development of a reliable computer aided diagnosis (CAD) system for lung cancer is one of the most important research topics in the area of medical image processing. Various CAD methods have been proposed to detect lung nodules. Giger et al. (2) detected nodules using multiple gray-level thresholding and a rule-based approach.
Ozekes et al REF segmented the lungs of the CT images using Genetic Cellular Neural Networks (G-CNN).
Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding
Korean Journal of Radiology
Korean Journal of Radiology
Medicine