Browse DORAS
Browse Theses
Search
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

Chowdhury, Tarik A. and Whelan, Paul F. and Ghita, Ovidiu (2006) The use of 3D surface fitting for robust polyp detection and classification in CT colonography. Computerized Medical Imaging and Graphics, 30 (8). pp. 427-436. ISSN 0895-6111

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
462Kb

Abstract

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:polyp detection; CT colonography; feature detection; least square fitting; low-dose;
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Publisher:Elsevier
Official URL:http://dx.doi.org/10.1016/j.compmedimag.2006.06.004
Copyright Information:Copyright © 2006 Elsevier Ltd
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:4682
Deposited On:07 Jul 2009 11:34 by DORAS Administrator. Last Modified 04 Feb 2010 13:30

Download statistics

Archive Staff Only: edit this record