Abstract
A technique for Computer Aided Detection (CAD) of colonic polyps, in Computed Tomographic (CT) Colonography is presented. Following the segmentation of the colonic wall, normal wall is identified using a fast geometric scheme able to approximate local curvature. The remaining structures are modeled using spin images and then compared to a set of existing polypoid models. Locations with the highest probability of being colonic polyps are labeled as final candidates. Models are computed by an unsupervised learning technique, using a leave one out technique on a study group of 50 datasets. True positive and false positive findings were determined, employing fiber optic colonoscopy as standard of reference. The detection rate for polyps larger than 6mm was above 85%, with an average false positive detection rate of 2.75 per case. The overall computation time for the method is approximately 6 minutes. Initial results show that Computer Aided Detection is feasible and that our method holds potential for screening purposes.
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Tumori Aparato Gastroenterico, Dati per la pianificazione dell’assistenza Edited by CPO Piemonte (May 1998)
Morson, B.C.: Factors Influencing the Prognosis of Early Cancer in the Rectum. Proc. R. Soc. Med. 59, 607–608 (1966)
Colorectal cancer - Oncology Channel. , http://www.oncologychannel.com /coloncancer/
Vining, D.J., et al.: Virtual colonoscopy. Radiology 193, 446 (1994) (abstract)
Summers, R.M., et al.: An Automated Polyp Detector for CT Colonography - Feasibility Study. Radiology, 284–290 (2000)
Yoshida, H., Nappi, J.: 3-D Computer-Aided Diagnosis Scheme for Detection of Colonic Polyps. IEEE Transactions on Medical Imaging, 1261–1274 (2001)
Kiss, G., et al.: Computer Aided Detection of Colonic Polyps via Geometric Features Classification. In: Proceedings 7th International Workshop on Vision, Modeling, and Visualization, pp. 27–34 (2002)
Ballard, D.M., Brown, C.M.: Computer Vision, pp. 123–166. Prentice Hall, Englewood Cliffs (1982)
Wiemker, R., Pekar, V.: Fast Computation of Isosurface Contour Spectra for Volume Visualization. In: Proceedings Computer Assisted Radiology and Surgery CARS (2001)
Andrew, J.: Spin-Images: A Representation for 3-D Surface Matching. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA (August 1997)
Unsupervised learning http://www.cs.mdx.ac.uk /staffpages /serengul /ML /unsupervised. html
Kiss, G., et al.: Computer Aided Detection in CT Colonography. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 746–753. Springer, Heidelberg (2003)
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Kiss, G., Van Cleynenbreugel, J., Marchal, G., Suetens, P. (2004). Computer Aided Detection in CT Colonography, via Spin Images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30136-3_98
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DOI: https://doi.org/10.1007/978-3-540-30136-3_98
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