Adaptive Energy Selective Active Contour with Shape Priors for Nuclear Segmentation and Gleason Grading of Prostate Cancer

  • Sahirzeeshan Ali
  • Robert Veltri
  • Jonathan I. Epstein
  • Christhunesa Christudass
  • Anant Madabhushi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)

Abstract

Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all 3 terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images. Morphological features extracted from these segmentations were found to able to discriminate different Gleason grade patterns with a classification accuracy of 84% via a Support Vector Machine classifier. On average the AdACM model provided 100% savings in computational times compared to a non-optimized hybrid AC model involving a shape prior.

Keywords

Active Contour Active Contour Model Gleason Pattern Geodesic Active Contour Concavity Point 
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.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. International J. of Computer Vision, 321–331 (1987)Google Scholar
  2. 2.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int.J. Comput. Vision 22(1), 61–79 (1997)CrossRefMATHGoogle Scholar
  3. 3.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)CrossRefMATHGoogle Scholar
  4. 4.
    Fang, W., Chan, K.: Statistical Shape Influence in Geodesic Active Contours. IEEE CVPR 40(8), 2163–2172 (2007)MATHGoogle Scholar
  5. 5.
    Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Chan, T.: Level set based shape prior segmentation. IEEE CVPR 2, 1164–1170 (2005)Google Scholar
  7. 7.
    Veltri, R., Isharwal, S., Mille, M., Epstein, J.I., Partin, A.: Nuclear Roundness Variance Predicts Prostate Cancer Progression,Metastasis, and Death: A Prospective EvaluationWith up to 25 Years of Follow-Up After Radical Prostatectomy. The Prostate 70, 1333–1339 (2010)Google Scholar
  8. 8.
    Fatakdawala, H., Xu, J., Basavanhally, A., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Expectation Maximization driven Geodesic Active Contour with Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology. IEEE TBME 57(7), 1676–1689 (2010)Google Scholar
  9. 9.
    Zhang, Q., Pless, R.: Segmenting multiple familiar objects under mutual occlusion. In: ICIP (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sahirzeeshan Ali
    • 1
  • Robert Veltri
    • 2
  • Jonathan I. Epstein
    • 2
  • Christhunesa Christudass
    • 2
  • Anant Madabhushi
    • 1
  1. 1.Rutgers, The State University of New JerseyNew BrunswickUSA
  2. 2.The Johns Hopkins HospitalBaltimoreUSA

Personalised recommendations