IMPROVED FUZZY-C-MEANS FOR NOISY IMAGE SEGMENTATION

Moualhi Wafa, Ezzeddine Zagrouba

Abstract

Magnetic resonance (MR) imaging is an important diagnostic imaging technique to early detect abnormal changes in the bain tissues. However, a serious limitation of the MR images is the significant amount of noise which can lead to inaccuracte segmentation. In this paper, a robust segmentation method based on an improvement of the conventional Fuzzy-C-Means (FCM) by modifiying its membership function is realized. A neighborhood attraction depending on the relative location and features of neighboring pixels is incorporated into the membership function to make the method robust to noise. Simulated and real brain MR images with different noise levels are used to demonstrate the superiority of the proposed method compared to some other FCM-based methods.

References

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Paper Citation


in Harvard Style

Wafa M. and Zagrouba E. (2009). IMPROVED FUZZY-C-MEANS FOR NOISY IMAGE SEGMENTATION . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2009) ISBN 978-989-674-007-8, pages 74-78. DOI: 10.5220/0002234000740078


in Bibtex Style

@conference{sigmap09,
author={Moualhi Wafa and Ezzeddine Zagrouba},
title={IMPROVED FUZZY-C-MEANS FOR NOISY IMAGE SEGMENTATION},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2009)},
year={2009},
pages={74-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002234000740078},
isbn={978-989-674-007-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2009)
TI - IMPROVED FUZZY-C-MEANS FOR NOISY IMAGE SEGMENTATION
SN - 978-989-674-007-8
AU - Wafa M.
AU - Zagrouba E.
PY - 2009
SP - 74
EP - 78
DO - 10.5220/0002234000740078