scholarly journals Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy

2005 ◽  
Vol 32 (12) ◽  
pp. 3602-3616 ◽  
Author(s):  
Zigang Wang ◽  
Zhengrong Liang ◽  
Lihong Li ◽  
Xiang Li ◽  
Bin Li ◽  
...  
Author(s):  
MARCELO FIORI ◽  
PABLO MUSÉ ◽  
GUILLERMO SAPIRO

We present a computer-aided detection pipeline for polyp detection in Computer tomographic colonography. The first stage of the pipeline consists of a simple colon segmentation technique that enhances polyps, which is followed by an adaptive-scale candidate polyp delineation, in order to capture the appropriate polyp size. In the last step, candidates are classified based on new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The system is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy. We achieve 100% sensitivity for polyps larger than 6 mm in size with just 0.9 false positives per case, and 93% sensitivity with 2.8 false positives per case for polyps larger than 3 mm in size.


2006 ◽  
Author(s):  
Robert Van Uitert ◽  
Ingmar Bitter ◽  
Ronald M. Summers ◽  
J. Richard Choi ◽  
Perry J. Pickhardt

2005 ◽  
Vol 129 (6) ◽  
pp. 1832-1844 ◽  
Author(s):  
Ronald M. Summers ◽  
Jianhua Yao ◽  
Perry J. Pickhardt ◽  
Marek Franaszek ◽  
Ingmar Bitter ◽  
...  

Author(s):  
Thomas Mang ◽  
Wolfgang Schima ◽  
Andrea Maier ◽  
Peter Pokieser

2011 ◽  
pp. 1340-1359
Author(s):  
Dongqing Chen ◽  
Aly A. Farag ◽  
Robert L. Falk ◽  
Gerald W. Dryden

Colorectal cancer includes cancer of the colon, rectum, anus and appendix. Since it is largely preventable, it is extremely important to detect and treat the colorectal cancer in the earliest stage. Virtual colonoscopy is an emerging screening technique for colon cancer. One component of virtual colonoscopy, image preprocessing, is important for colonic polyp detection/diagnosis, feature extraction and classification. This chapter aims at an accurate and fast colon segmentation algorithm and a general variational-approach based framework for image pre-processing techniques, which include 3D colon isosurface generation and 3D centerline extraction for navigation. The proposed framework has been validated on 20 real CT Colonography (CTC) datasets. The average segmentation accuracy has achieved 96.06%, and it just takes about 5 minutes for a single CT scan of 512*512*440. All the 12 colonic polyps with sizes of 6 mm and above in the 20 clinical CTC datasets are found by this work.


Author(s):  
T. Mang ◽  
P. Pokieser ◽  
A. Maier ◽  
W. Schima

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