A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering

Saeed Mirghasemi, Ramesh Rayudu, Mengjie Zhang


Introducing methods that can work out the problem of noisy image segmentation is necessary for real-world vision problems. This paper proposes a new computational algorithm for segmentation of gray images contaminated with impulse noise. We have used Fuzzy C-Means (FCM) in fusion with Particle Swarm Optimization (PSO) to define a new similarity metric based on combining different intensity-based neighborhood features. PSO as a computational search algorithm, looks for an optimum similarity metric, and FCM as a clustering technique, helps to verify the similarity metric goodness. The proposed method has no parameters to tune, and works adaptively to eliminate impulsive noise. We have tested our algorithm on different synthetic and real images, and provided quantitative evaluation to measure effectiveness. The results show that, the method has promising performance in comparison with other existing methods in cases where images have been corrupted with a high density noise.


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Paper Citation

in Harvard Style

Mirghasemi S., Rayudu R. and Zhang M. (2015). A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 17-27. DOI: 10.5220/0005584500170027

in Bibtex Style

author={Saeed Mirghasemi and Ramesh Rayudu and Mengjie Zhang},
title={A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},

in EndNote Style

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering
SN - 978-989-758-157-1
AU - Mirghasemi S.
AU - Rayudu R.
AU - Zhang M.
PY - 2015
SP - 17
EP - 27
DO - 10.5220/0005584500170027