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Technical Program
Paper Detail
| Paper: | TP-P3.4 |
| Session: | Biomedical Image Segmentation and Quantitative Analysis |
| Time: | Tuesday, October 10, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
COMPUTERIZED DETECTION OF LUNG NODULES WITH AN ENHANCED FALSE POSITIVE REDUCTION SCHEME |
| Authors: |
Negar Memarian; Ryerson University | | | | | Javad Alirezaie; Ryerson University | | | | | Paul Babyn; University of Toronto | | |
| Abstract: |
Computed tomography (CT) scan of lungs produces high volume of data, which is difficult to assess manually. Hence, computer-aided detection (CAD) of pulmonary nodules has become a major area of interest in biomedical imaging. Reducing the number of false positives (FPs) is considered a high priority for enhancement of any CAD system. Here we report a novel hybrid learning scheme for reducing the number of FPs in a computerized lung nodule detection system. This novel scheme consists of two main stages, namely fuzzy c-means clustering and iterative linear discriminant analysis. The main advantage of the proposed iterative linear discriminant analysis is its case adaptive nature designed to maintain a good level of sensitivity. We compare the results obtained from this hybrid scheme with a rule-based FP reduction approach and show the superiority of our novel scheme. |
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