An Improved 2D Colonic Polyp Segmentation Framework Based on Gradient Vector Flow Deformable Model

  • Dongqing Chen
  • M. Sabry Hassouna
  • Aly A. Farag
  • Robert Falk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


Computed Tomography Colonography has been proved to be a valid technique for detecting and screening colorectal cancers. In this paper, we present a framework for colonic polyp detection and segmentation. Firstly, we propose to use four different geometric features for colonic polyp detection, which include shape index, curvedness, sphericity ratio and the absolute value of inner product of maximum principal curvature and gradient vector flow. Then, we use the bias-corrected fuzzy c-mean algorithm and gradient vector flow based deformable model for colonic polyp segmentation. Finally, we measure the overlap between the manual segmentation and the algorithm segmentation to test the accuracy of our frame work. The quantitative experiment results have shown that the average overlap is 85.17%±3.67%.


Active Contour Principal Curvature Hounsfield Unit Manual Segmentation Colonic Polyp 
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  1. 1.
    Abbruzzese, J., Pollock, R.: Gastrointestinal cancer. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Yao, J., Miller, M., Franazek, M., Summers, R.M.: Colonic polyp segmetattion in CT colonography-based on fuzzy clustering and deformable models. IEEE Transactions on Medical Imaging 23, 1344–1352 (2004)CrossRefGoogle Scholar
  3. 3.
    Yao, J., Summers, R.M.: Three-dimensional colonic polyp segmentation using dynamic deformable surfaces, pp. 280–289 (2004)Google Scholar
  4. 4.
    Yao, J., Miller, M., Summers, R.M.: Automatic segmentation and detection of colonic polyps in CT colonography based on knowledge-guided deformable models, vol. 5031, pp. 370–380 (2003)Google Scholar
  5. 5.
    Yoshida, H., Näppi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Transactions on Medical Imaging 20, 1261–1274 (2001)CrossRefGoogle Scholar
  6. 6.
    Acar, B., Napel, S.: Edge displacement field-based classification for improved detection of polyps in CT colonography. IEEE Transactions on Medical Imaging 21, 1461–1467 (2002)CrossRefGoogle Scholar
  7. 7.
    Xu, C., Pham, D.L., Prince, J.L.: Finding the brain cortex using fuzzy segmentation, isosurface, and deformable surface model, pp. 399–404 (1997)Google Scholar
  8. 8.
    Cohen, L.D.: On active contour models and ballons. Computer Vision, Graphics, Image Processing: Image Understanding 53, 211–218 (1991)MATHGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes, active contour model. International Journal of Computer Vision 1, 321–331 (1987)CrossRefGoogle Scholar
  10. 10.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Transaction on Image Processing 7, 359–369 (1998)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Ahmed, M.N., Yamany, S.M., Farag, A.A.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transaction on Medical Imaging 21, 193–199 (2002)CrossRefGoogle Scholar
  12. 12.
    Taubin, G.: Estiamting the tensor of curvature of a surface from a polyhedral approximation. In: Procceding of the Fifth International Conference on Computer Vision (ICCV 1995), pp. 902–907 (1995)Google Scholar
  13. 13.
    Farag, A.A., Hassouna, M.S., Falk, R.: Differential fly-throughs (dft): A general framework for computing flight paths. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 654–661. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dongqing Chen
    • 1
  • M. Sabry Hassouna
    • 1
  • Aly A. Farag
    • 1
  • Robert Falk
    • 2
  1. 1.Computer Vision and Image Processing (CVIP) Lab, Department of Electrical and Computer EngineeringUniversity of LouisvilleLouisvilleUSA
  2. 2.Department of Medial ImagingJewish HospitalLouisvilleUSA

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