Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model

Yang, X., Beddoe, G. & Slabaugh, G.G. (2011). Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model. Lecture Notes in Computer Science, 6668, pp. 53-59. doi: 10.1007/978-3-642-25719-3_8

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The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, trained using kernel density estimation (KDE) 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. Our method is validated using a diverse database, including data from five hospitals. The experiments demonstrate a high detection rate of the RT path, and when tested in a CAD system, reduce 20.3% of the FPs with no loss of CAD sensitivity.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25719-3_8
Uncontrolled Keywords: Rectal Tube, RANSAC, CAD, CT colonography
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/6334

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