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Shape Filtering for False Positive Reduction at Computed Tomography Colonography

  • Abhilash A. Miranda
  • Tarik A. Chowdhury
  • Ovidiu Ghita
  • Paul F. Whelan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

In this paper, we treat the problem of reducing the false positives (FP) in the automatic detection of colorectal polyps at Computer Aided Detection in Computed Tomography Colonography (CAD-CTC) as a shape-filtering task. From the extracted candidate surface, we obtain a reliable shape distribution function and analyse it in the Fourier domain and use the resulting spectral data to classify the candidate surface as belonging to a polyp or a non-polyp class. The developed shape filtering scheme is computationally efficient (takes approximately 2 seconds per dataset to detect the polyps from the colonic surface) and offers robust polyp detection with an overall false positive rate of 5.44 per dataset at a sensitivity of 100% for polyps greater than 10mm when it was applied to standard and low dose CT data.

Keywords

Power Spectral Density Compute Tomography Data Colorectal Polyp Polyp Detection Phantom Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    World Health Organisation: The World Health Report 2004. WHO (2004)Google Scholar
  2. 2.
    Chowdhury, T., Ghita, O., Whelan, P.: A Statistical approach for Robust Polyp Detection in CT Colonography. In: Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 27 (2005)Google Scholar
  3. 3.
    Zucker, S.W., Hummel, R.A.: A Three-Dimensional Edge Operator. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 324–331 (1981)MATHCrossRefGoogle Scholar
  4. 4.
    Kiss, G., Cleynenbreugel, J., Thomeer, M., Suetens, P., Marchal, G.: Computer Aided Diagnosis for Virtual Colonography. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 621–628. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Summers, R., Johnson, C., Pusanik, L., Malley, J., Youssef, A., Reed, J.: Automated Polyp Detection at CT Colonography: Feasibility Assessment in a Human Population. Radiology 216, 284–290 (2000)Google Scholar
  6. 6.
    Vining, D., Hunt, G., Ahn, D., Steltes, D., Helmer, P.: Computer-Assisted Detection of Colon Polyps and Masses. Radiology 219, 51–59 (2001)Google Scholar
  7. 7.
    Yoshida, H., Masutani, Y., MacEneaney, P., Rubin, D., Dachman, A.: Computerized Detection of Colonic Polyps at CT Colonography on the Basis of Volumetric Features: Pilot Study. Radiology 222, 327–336 (2002)CrossRefGoogle Scholar
  8. 8.
    Osada, R., Funkhouser, T., Dobkin, D.: Matching 3D Models with Shape Distributions. International Conference on Shape Modeling and Applications, pp. 154–166 (2001)Google Scholar
  9. 9.
    Chowdhury, T., Sadleir, R., Whelan, P., Moss, A., Varden, J., Short, M., Fenlon, H., MacMathuna, P.: The Impact of Radiation Dose on Imaged Polyp Characteristics at CT Colonography: Experiments with a Synthetic Phantom. Technical report, Association of Physical Scientists in Medicine, Annual Scientific Meeting, Dublin (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Abhilash A. Miranda
    • 1
  • Tarik A. Chowdhury
    • 1
  • Ovidiu Ghita
    • 1
  • Paul F. Whelan
    • 1
  1. 1.Vision Systems GroupDublin City UniversityIreland

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