Segmentation of Medical Images using Fuzzy Mathematical Morphology

Authors

  • A. Bouchet Measurement and Signal Processing Laboratory, School of Engineering, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina
  • Juan Ignacio Pastore Measurement and Signal Processing Laboratory, School of Engineering, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina
  • Virginia Laura Ballarín Measurement and Signal Processing Laboratory, School of Engineering, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina

Keywords:

Fuzzy Logic, Mathematical morphology, Segmentation

Abstract

Currently, Mathematical Morphology (MM) has become a powerful tool in Digital Image Processing (DIP). It allows processing images to enhance fuzzy areas, segment objects, detect edges and analyze structures. The techniques developed for binary images are a major step forward in the application of this theory to gray level images. One of these techniques is based on fuzzy logic and on the theory of fuzzy sets. Fuzzy sets have proved to be strongly advantageous when representing inaccuracies, not only regarding the spatial localization of objects in an image but also the membership of a certain pixel to a given class. Such inaccuracies are inherent to real images either because of the presence of indefinite limits between the structures or objects to be segmented within the image due to noisy acquisitions or directly because they are inherent to the image formation methods. Our approach is to show how the fuzzy sets specifically utilized in MM have turned into a functional tool in DIP.

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References

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Published

2007-10-01

How to Cite

Bouchet, A., Pastore, J. I., & Ballarín, V. L. (2007). Segmentation of Medical Images using Fuzzy Mathematical Morphology. Journal of Computer Science and Technology, 7(03), p. 256–262. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/779

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Section

Original Articles