Low Level Statistical Models for Initialization of Interactive 2D/3D Segmentation Algorithms

Jan Kolomazník, Jan Horáček, Josef Pelikán

Abstract

In this paper we present two models which are suitable for interactive segmentation algorithms to decrease amount of user work. Models are used during initialization step and do not increase complexity of segmentation algorithms. Model describe spatial distribution of image values and classification as either foreground or background. Second part of the model is vector field which constrains direction of boundary normals. We show how to use these models in parametric snakes/surfaces framework and minimal graph-cut based segmentation.

References

  1. Boykov, Y. and Jolly, M.-P. (2001). Interactive graph cuts for optimal boundary amp; region segmentation of objects in n-d images. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 1, pages 105-112 vol.1.
  2. Cremers, D., Rousson, M., and Deriche, R. (2007). A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. International Journal of Computer Vision, 72:215.
  3. Heimann, T. and Meinzer, H.-P. (2009). Statistical shape models for 3d medical image segmentation: A review. Medical Image Analysis, 13(4):543 - 563.
  4. Jacob, M., Blu, T., and Unser, M. (2001). A unifying approach and interface for spline-based snakes. In in Proc. SPIE Med. Imaging, l. 4322, pages 340-347.
  5. Jacob, M., Blu, T., and Unser, M. (2004). Efficient energies and algorithms for parametric snakes. IEEE Transactions on Image Processing, 13(9):1231-1244.
  6. Kolomaznik, J., Horacek, J., Krajicek, V., and Pelikan, J. (2012). Implementing interactive 3d segmentation on cuda using graph-cuts and watershed transformation. International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision.
  7. Leventon, M., Grimson, W., and Faugeras, O. (2002). Statistical shape influence in geodesic active contours. In Biomedical Imaging, 2002. 5th IEEE EMBS International Summer School on, page 8 pp.
  8. Okada, T., Yokota, K., Hori, M., Nakamoto, M., Nakamura, H., and Sato, Y. (2008). Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from ct images. In Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I, MICCAI 7808, pages 502-509, Berlin, Heidelberg. Springer-Verlag.
  9. Paragios, N., Mellina-Gottardo, O., and Ramesh, V. (2001). Gradient vector flow fast geodesic active contours. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 1, pages 67 -73 vol.1.
  10. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. E., and Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. IEEE transactions on medical imaging, 22(2):137-154.
  11. Xu, C. and Prince, J. L. (1997). Gradient vector flow: A new external force for snakes. In IEEE Proc. Conf. On, pages 66-71.
  12. Yao, J. and Summers, R. (2009). Statistical location model for abdominal organ localization. In Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., and Taylor, C., editors, Medical Image Computing and ComputerAssisted Intervention - MICCAI 2009, volume 5762 of Lecture Notes in Computer Science, pages 9-17. Springer Berlin, Heidelberg.
  13. Yi, F. and Moon, I. (2012). Image segmentation: A survey of graph-cut methods. In International Conference on Systems and Informatics.
  14. Yue, Y. and Tagare, H. (2009). Learning to segment using machine-learned penalized logistic models. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 0:58-65.
  15. Zhao, F. and Xie, X. (2013). An overview of interactive medical image segmentation. Annals of the BMVA, 2013(7):1-22.
Download


Paper Citation


in Harvard Style

Kolomazník J., Horáček J. and Pelikán J. (2015). Low Level Statistical Models for Initialization of Interactive 2D/3D Segmentation Algorithms . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 686-692. DOI: 10.5220/0005361506860692


in Bibtex Style

@conference{visapp15,
author={Jan Kolomazník and Jan Horáček and Josef Pelikán},
title={Low Level Statistical Models for Initialization of Interactive 2D/3D Segmentation Algorithms},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={686-692},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005361506860692},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Low Level Statistical Models for Initialization of Interactive 2D/3D Segmentation Algorithms
SN - 978-989-758-089-5
AU - Kolomazník J.
AU - Horáček J.
AU - Pelikán J.
PY - 2015
SP - 686
EP - 692
DO - 10.5220/0005361506860692