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- W2154790985 abstract "A computer-aided detection (CAD) system was developed for detecting lung nodule patterns, which generally appear as circular areas of high opacity on serial-section Computed tomography (CT) images. First of all, rule-based segmentation of lung region was performed. Then, our method detected the regions of interest (ROIs) using the density values of pixels in CT images and scanning the pixels in 8 directions by using various thresholds. Then to reduce the number of ROIs the amounts of change in their locations based on the upper and the lower slices were examined, and finally a nodule template based algorithm was employed to categorize the ROIs according to their morphologies. To calculate the parameters of the template, a genetic algorithm process was employed as an optimization method. To test the system’s efficiency, we applied it to 276 normal and abnormal CT images of 12 patients with 153 nodules. The experimental results showed that the system achieved 93.4% sensitivity with 0.594 false positives per image." @default.
- W2154790985 created "2016-06-24" @default.
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- W2154790985 date "2007-01-01" @default.
- W2154790985 modified "2023-09-27" @default.
- W2154790985 title "RULE-BASED LUNG REGION SEGMENTATION AND NODULE DETECTION VIA GENETIC ALGORITHM TRAINED TEMPLATE MATCHING" @default.
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