TY  - JOUR
ID  - city4703
UR  - https://openaccess.city.ac.uk/id/eprint/4703/
IS  - 11
A1  - Yang, X.
A1  - Slabaugh, G. G.
N2  - Purpose: The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. A robust and efficient detection of RT can improve CAD performance by eliminating such ?obvious? FPs and increase radiologists? confidence in CAD. Methods: In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, trained using kernel density estimation on simple low-level features, are employed to rank and select the most likely RT tube candidate on each axial slice. Then, a shape model, robustly estimated using Random Sample Consensus (RANSAC), infers the global RT path from the selected local detections. Subimages around the RT path are projected into a subspace formed from training subimages of the RT. A quadratic discriminant analysis (QDA) provides a classification of a subimage as RT or non-RT based on the projection. Finally, a bottom-top clustering method is proposed to merge the classification predictions together to locate the tip position of the RT. Results: Our method is validated using a diverse database, including data from five hospitals. On a testing data with 21 patients (42 volumes), 99.5% of annotated RT paths have been successfully detected. Evaluated with CAD, 98.4% of FPs caused by the RT have been detected and removed without any loss of sensitivity. Conclusion: The proposed method demonstrates a high detection rate of the RT path, and when tested in a CAD system, reduces FPs caused by the RT without the
loss of sensitivity.
VL  - 38
TI  - A robust and efficient approach to detect 3D rectal tubes from CT colonography
AV  - public
EP  - 6247
Y1  - 2011/10/27/
PB  - Wiley
JF  - Medical Physics
KW  - Rectal Tube
KW  -  RANSAC
KW  -  CAD
KW  -  eigenspace
KW  -  CT colonography
SN  - 0094-2405
SP  - 6238
ER  -