PURPOSE: A large number of false positives (FPs) generated by computer-aided detection (CAD) schemes is likely to distract radiologists' attention and decrease their interpretation efficiency. This study aims to develop projection-based features which characterize true and false positives to increase the specificity while maintaining high sensitivity in detecting colonic polyps. METHODS: In this study, two-dimensional projection images are obtained from each initial polyp candidate or volume of interest, and features are extracted from both the gray and color projection images to differentiate FPs from true positives. These projection features were tested to exclude different types of FPs, such as haustral folds, rectal tubes, and residue stool using a database of 325 patient studies (from two different institutions), which includes 556 scans at supine and/or prone positions with 347 polyps and masses sized from 5 to 60 mm. For comparison, several well-established features were used to generate a baseline reference. The experimental evaluation was conducted for large polyps (> or = 10 mm) and medium-sized polyps (5-9 mm) separately. RESULTS: For large polyps, the additional usage of the projection features reduces the FP rate from 5.31 to 1.92 per scan at the comparable by-polyp sensitivity level of 93.1%. For medium-sized polyps, the FP rate is reduced from 8.89 to 5.23 at the sensitivity level of 80.6%. The percentages of FP reduction are 63.9% and 41.2% for the large and medium-sized polyps, respectively, without sacrificing detection sensitivity. CONCLUSIONS: The results have demonstrated that the new projection features can effectively reduce the FPs and increase the detection specificity without sacrificing the sensitivity. CAD of colonic polyps is supposed to help radiologists to improve their performance in interpreting computed tomographic colonography images.
PURPOSE: A large number of false positives (FPs) generated by computer-aided detection (CAD) schemes is likely to distract radiologists' attention and decrease their interpretation efficiency. This study aims to develop projection-based features which characterize true and false positives to increase the specificity while maintaining high sensitivity in detecting colonic polyps. METHODS: In this study, two-dimensional projection images are obtained from each initial polyp candidate or volume of interest, and features are extracted from both the gray and color projection images to differentiate FPs from true positives. These projection features were tested to exclude different types of FPs, such as haustral folds, rectal tubes, and residue stool using a database of 325 patient studies (from two different institutions), which includes 556 scans at supine and/or prone positions with 347 polyps and masses sized from 5 to 60 mm. For comparison, several well-established features were used to generate a baseline reference. The experimental evaluation was conducted for large polyps (> or = 10 mm) and medium-sized polyps (5-9 mm) separately. RESULTS: For large polyps, the additional usage of the projection features reduces the FP rate from 5.31 to 1.92 per scan at the comparable by-polyp sensitivity level of 93.1%. For medium-sized polyps, the FP rate is reduced from 8.89 to 5.23 at the sensitivity level of 80.6%. The percentages of FP reduction are 63.9% and 41.2% for the large and medium-sized polyps, respectively, without sacrificing detection sensitivity. CONCLUSIONS: The results have demonstrated that the new projection features can effectively reduce the FPs and increase the detection specificity without sacrificing the sensitivity. CAD of colonic polyps is supposed to help radiologists to improve their performance in interpreting computed tomographic colonography images.
Authors: Ronald M Summers; Jianhua Yao; Perry J Pickhardt; Marek Franaszek; Ingmar Bitter; Daniel Brickman; Vamsi Krishna; J Richard Choi Journal: Gastroenterology Date: 2005-12 Impact factor: 22.682
Authors: Thomas M Gluecker; C Daniel Johnson; William S Harmsen; Kenneth P Offord; Ann M Harris; Lynn A Wilson; David A Ahlquist Journal: Radiology Date: 2003-05 Impact factor: 11.105
Authors: Matthew T McKenna; Shijun Wang; Tan B Nguyen; Joseph E Burns; Nicholas Petrick; Ronald M Summers Journal: Med Image Anal Date: 2012-05-03 Impact factor: 8.545
Authors: Shijun Wang; Nicholas Petrick; Robert L Van Uitert; Senthil Periaswamy; Zhuoshi Wei; Ronald M Summers Journal: IEEE Trans Inf Technol Biomed Date: 2012-04-27
Authors: Shijun Wang; Matthew T McKenna; Tan B Nguyen; Joseph E Burns; Nicholas Petrick; Berkman Sahiner; Ronald M Summers Journal: IEEE Trans Med Imaging Date: 2012-05 Impact factor: 10.048