FUZZY CONNECTEDNESS IN SEGMENTATION OF MEDICAL IMAGES - A Look at the Pros and Cons

Pawel Badura, Jacek Kawa, Joanna Czajkowska, Marcin Rudzki, Ewa Pietka

Abstract

An attempt to recapitulate and conclude numerous experiences with the fuzzy connectedness theory applied to medical image segmentation is made in this paper. The fuzzy connectedness principles introduced in 1996 have been developed and tested in dozens of studies in past 15 years; many advantages, as well as shortcomings have been discovered and described. Some aspects of the method and its applications have been summarized here, including the examples of specific 2D and 3D medical studies with various objects, subjected to fuzzy connected segmentation. Deliberation about the usefulness of multiseeded and multiobject variants is also present. An algorithm optimized for matrix computations-based programming languages is introduced. Finally, 3 fuzzy connectedness-based computer aided diagnosis systems are described and evaluated.

References

  1. Abrahams, J. M., Saha, P. K., Hurst, R. W., LeRoux, P. D., and Udupa, J. K. (2002). Three-Dimensional Bone-Free Rendering of the Cerebral Circulation Using Computed Tomographic Angiography and Fuzzy Connectedness. Neurosurgery, 51:264-269.
  2. Admasu, F., Al-Zubi, S., Toennies, K., Bodammer, N., and Hinrichs, H. (2003). Segmentation of Multiple Sclerosis Lesions from MR Brain Images Using the Principles of Fuzzy-Connectedness and Artificial Neuron Networks. In International Conference on Image Processing, ICIP 2003, volume 2, pages II 1081-1084.
  3. Badura, P. and Pietka, E. (2007). Semi-Automatic Seed Points Selection in Fuzzy Connectedness Approach to Image Segmentation. In Computer Recognition Systems 2 (Advances in Soft Computing), volume 45, pages 679-686. Springer-Verlag.
  4. Badura, P. and Pietka, E. (2008). Pre- and Postprocessing Stages in Fuzzy Connectedness-Based Lung Nodule CAD. In Information Technologies in Biomedicine (Advances in Soft Computing), volume 47, pages 192- 199. Springer-Verlag.
  5. Carvalho, B. M., Joe Gau, C., Herman, G. T., and Yung Kong, T. (1999). Algorithms for Fuzzy Segmentation. Pattern Analysis & Applications, 2:73-81.
  6. Ciesielski, K. C., Udupa, J. K., Saha, P. K., and Zhuge, Y. (2007). Iterative Relative Fuzzy Connectedness for Multiple Objects with Multiple Seeds. Comput. Vis. Image Underst., 107(3):160-182.
  7. Czajkowska, J., Badura, P., and Pietka, E. (2010). 4D Segmentation of Ewing's Sarcoma in MR Images. In Information Technologies in Biomedicine: Volume 2 (Advances in Intelligent and Soft Computing), volume 69, pages 91-100. Springer-Verlag.
  8. Dehmeshki, J., Amin, H., Valdivieso, M., and Ye, X. (2008). Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach. IEEE Transactions on Medical Imaging, 27(4):467-480.
  9. Gonzalez, R. and Woods, R. (2002). Digital Image Processing. Prentice Hall, Upper Saddle River, NJ.
  10. Herman, G. T. and Carvalho, B. M. (2001). Multiseeded Segmentation Using Fuzzy Connectedness. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(5):460-474.
  11. Kawa, J. and Pietka, E. (2008). Automated FuzzyConnectedness-Based Segmentation in Extraction of Multiple Sclerosis Lesions. In Information Technologies in Biomedicine (Advances in Soft Computing), volume 47, pages 149-156. Springer-Verlag.
  12. Moonis, G., Liu, J., Udupa, J. K., and Hackney, D. (2002). Estimation of Tumor Volume Using Fuzzy Connectedness Segmentation of MRI. Am. J. Neuroradiol., 23(3):356-363.
  13. Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639.
  14. Saha, P. K. and Udupa, J. K. (2001). Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds. Computer Vision and Image Understanding, 83(3):275-295.
  15. Saha, P. K., Udupa, J. K., and Odhner, D. (2000). ScaleBased Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation. Computer Vision and Image Understanding, 77(9):145-174.
  16. Tschirren, J., Hoffman, E. A., McLennan, G., and Sonka, M. (2005). Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis from Low-Dose CT Scans. IEEE Transactions on Medical Imaging, 24(12):1529-1539.
  17. Udupa, J. K., Odhner, D., and Eisenberg, H. C. (2001). New Automatic Mode of Visualizing the Colon via CT. In SPIE: Medical Imaging, volume 4319, pages 237-243, San Diego, CA.
  18. Udupa, J. K., Saha, P., and Lotufo, R. (2002). Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11):1485-1500.
  19. Udupa, J. K. and Samarasekera, S. (1996). Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation. Graphical Models and Image Processing, 58(3):246-261.
  20. Udupa, J. K., Wei, L., Samarasekera, S., Miki, Y., van Buchem, M. A., and Grossman, R. I. (1997). Multiple Sclerosis Lesion Quantification Using FuzzyConnectedness Principles. IEEE Transactions on Medical Imaging, 16(5):598-609.
Download


Paper Citation


in Harvard Style

Badura P., Kawa J., Czajkowska J., Rudzki M. and Pietka E. (2011). FUZZY CONNECTEDNESS IN SEGMENTATION OF MEDICAL IMAGES - A Look at the Pros and Cons . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 486-492. DOI: 10.5220/0003670904860492


in Bibtex Style

@conference{fcta11,
author={Pawel Badura and Jacek Kawa and Joanna Czajkowska and Marcin Rudzki and Ewa Pietka},
title={FUZZY CONNECTEDNESS IN SEGMENTATION OF MEDICAL IMAGES - A Look at the Pros and Cons},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},
year={2011},
pages={486-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003670904860492},
isbn={978-989-8425-83-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
TI - FUZZY CONNECTEDNESS IN SEGMENTATION OF MEDICAL IMAGES - A Look at the Pros and Cons
SN - 978-989-8425-83-6
AU - Badura P.
AU - Kawa J.
AU - Czajkowska J.
AU - Rudzki M.
AU - Pietka E.
PY - 2011
SP - 486
EP - 492
DO - 10.5220/0003670904860492