Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography

Author(s):  
Hongbin Zhu ◽  
Matthew Barish ◽  
Perry Pickhardt ◽  
Yi Fan ◽  
Erica Posniak ◽  
...  
Author(s):  
Xujiong Ye ◽  
Greg Slabaugh

This chapter presents an automated method to identify colonic polyps and suppress false positives for Computer-Aided Detection (CAD) in CT Colonography (CTC). The method formulates the problem of polyp detection as a probability calculation through a unified Bayesian statistical approach. The polyp likelihood is modeled with a combination of shape, intensity, and location features, while also taking into account the spatial prior probability encoded by a Markov Random Field. A second principal curvature PDE provides a shape model; and partial volume effect is incorporated in the intensity model. When evaluated on a large multi-center dataset of colonic CT scans, the CAD detection performance as well as the volume overlap ratio demonstrate the potential of the proposed method. The method results in an average 24% reduction of false positives with no impact on sensitivity. The method is also applicable to generation of initial candidates for CTC CAD with high detection sensitivity and relatively lower false positives, compared to other non-Bayesian methods.


2011 ◽  
Vol 18 (8) ◽  
pp. 1024-1034 ◽  
Author(s):  
Hongbin Zhu ◽  
Yi Fan ◽  
Hongbing Lu ◽  
Zhengrong Liang

2009 ◽  
Vol 192 (6) ◽  
pp. 1682-1689 ◽  
Author(s):  
Stuart A. Taylor ◽  
John Brittenden ◽  
James Lenton ◽  
Hannah Lambie ◽  
Anthony Goldstone ◽  
...  

2014 ◽  
Vol 24 (7) ◽  
pp. 1466-1476 ◽  
Author(s):  
Thomas Mang ◽  
Luca Bogoni ◽  
Vikram X. Anand ◽  
Dass Chandra ◽  
Andrew J. Curtin ◽  
...  

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