Rough representation of a region of interest in medical images

@article{Hirano2005RoughRO,
  title={Rough representation of a region of interest in medical images},
  author={Shoji Hirano and Shusaku Tsumoto},
  journal={Int. J. Approx. Reasoning},
  year={2005},
  volume={40},
  pages={23-34}
}
This paper introduces the rough representation of a region of interest (ROI) in medical images. The main advantage of this method is its ability to represent inconsistency between the knowledge-driven shape and image-driven shape of a ROI using rough approximations. The method consists of three steps including preprocessing. First, we derive discretized attribute values that describe the characteristics of a ROI. Next, using all attributes, we build up the basic regions in the image so that… CONTINUE READING
Highly Cited
This paper has 43 citations. REVIEW CITATIONS
22 Citations
11 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 22 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 11 references

LERS—a system for learning from examples based on rough sets

  • J. W. Grzymala-Busse
  • in: R. Slowinski (Ed.), Intelligent Decision…
  • 2005
1 Excerpt

A

  • S. K. Pal
  • Pal (eds): Pattern recognition, from classical to…
  • 2001
1 Excerpt

Similar Papers

Loading similar papers…