Paper
9 March 2010 Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography
Hongbin Zhu, Matthew Barish, Perry Pickhardt, Yi Fan, Erica Posniak, Robert Richards, Zhengrong Liang
Author Affiliations +
Abstract
A large number of false positives (FPs) generated by computer-aided detection schemes is likely to distract radiologists' attention and decreases their interpretation efficiency. Therefore, it is desirable to reduce FPs as many as possible to increase the detection specificity while maintaining the high detection sensitivity. In this paper, several features are extracted from the projected images of each initial polyp candidate to differentiate FPs from true positives. These features demonstrate the potential to exclude different types of FPs, like haustral folds, rectal tubes and residue stool by an evaluation 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 purpose, several wellestablished features are used to generate a baseline reference. At the by-polyp detection sensitivity level of 96% (no loss of detection sensitivity), the number of FPs per scan is 7.8 by the baseline and 3.75 if the new projection features are added, which is a reduction of 51.9% FPs from the baseline.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hongbin Zhu, Matthew Barish, Perry Pickhardt, Yi Fan, Erica Posniak, Robert Richards, and Zhengrong Liang "Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240B (9 March 2010); https://doi.org/10.1117/12.844467
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Cited by 1 scholarly publication.
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KEYWORDS
Laser range finders

Tissues

Feature extraction

Databases

Virtual colonoscopy

3D image processing

Computed tomography

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