LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: IEEE
Languages: English
Types: Unknown
Subjects: RC, QA75
The ileocecal valve (ICV) is a common source of false-positive (FP) detections in CT colonography (CTC) computer-aided detection (CAD) of polyps. In this paper, we propose an automatic method to identify ICV CAD regions to reduce FPs. The ICV is a particularly challenging structure to detect due to its variable, polyp-mimicking morphology. However, the vast majority of ICVs have a visible orifice, which appears as a 3D concave region. Our method identifies the orifice concave region using a partial differential equation (PDE) based on 3D curvature and geometric constraints. These orifice features, combined with intensity and shape features generated in a Bayesian framework, comprise a set of compact features fed into an Adaboost classifier to produce a final classification of a region being ICV or non-ICV. Experimental results on a multi-center tagged CTC dataset demonstrate the success of the method in detecting ICV regions and reducing FPs in CAD.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] D.Regge, T.M.Gallo, G.Nieddu, G.Galatola, M.Fracchia, E.Neri, P.Vagli, and C.Bartolozzi, “IIeocecal valve imaging on computed tomographic colonography”, Abdominal Imaging, 30:20-25, 2004.
    • [2] E.M.Lawrence, P.J.Pickhardt, D.H.Kim and J.B.Robbins, “Colorectal polyps: stand-alone performance of computer-aided detection in a large asymptomatic screening population”, Radiology, 256 (3): 791-798, Sep, 2010.
    • [3] G.Slabaugh, X.Yang, X.Ye, R.Boyes, and G.Beddoe, “A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions”, Algorithms, 3(1): 21-43, 2010.
    • [4] R.M.Summers, J.Yao and C.D.Johnson, “CT Colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections”, Radiology, 10, 2004.
    • [5] L.Lu, A.Barbu, M.Wolf, J.Liang, L.Bogoni, M.Salganicoff and D.Comaniciu, “Simultaneous detection and registration for ileocecal valve detection in 3D CT colonography”, ECCV, 2006.
    • [6] C.van Wijk, V.F.van Ravesteijn, F.M.Vos and L.J.van Vliet,“ Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow”, IEEE Trans. Medical Imaging, 29 (3). 688-698, 2010.
    • [7] Y.Zhou and A.W.Toga, “Efficient skeletonization of volumetric objects”, IEEE Trans. Visualization and computer graphics, 5(3), 196-209, 1999.
    • [8] Y.Freund, R.E.Scharpire. “A decision-theoretic generalization of on-line learning and an applization to boosting”, Journal of computer and system sciences, 55, 119-139, 1997.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article