ICIP 2006, Atlanta, GA
 

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Atlanta Conv. & Vis. Bureau

 

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
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.