IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BASED ON PSO

Liping Zheng, Guangyao Li, Jing Liang, Quanke Pan

2009

Abstract

Image segmentation plays an important role in the field of image processing. Threshold segmentation is a simple and important method in image segmentation. Maximum Entropy is a common threshold segmentation method. In order to adequately utilize gray information and spatial information of image, an improved 2D entropy computation method is proposed. Otherwise, Particle Swarm Optimization(PSO) algorithm is used to solve maximum of improved entropy. Maximum takes as the optimal image segmentation threshold. In this paper, two CT images were segmented in experiment. Experimental results show that this method can quickly and accurately obtain segmentation threshold. Otherwise, this method has strong anti-noise capability and save computation time.

References

  1. Pal N R., Pal S K., 1993. A review on image segmentation techniques. Pattern Recognition.
  2. Glasbey, CA., 1993. An analysis of histogram-based thresholding algorithms, CVGIP: Graphical Models and Image Process.
  3. Otsu N., 1979. A threshold value selection method from grey-level histograms, IEEE Trans. System.
  4. Kittler J. Illingworth M., 1986. Minimum error thresholding. Pattern Recognition.
  5. Pun T.,1980. A new method for grey-level picture thresholding using the entropy of the histogram. Signal Process.
  6. Kapur J. N., Sahoo P.K., Wong A., 1985. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics and Image Processing.
  7. Abutaleb A S.,1989. Automatic thresholding of gray-level picture using two-dimensional entropies. Pattern Recognition.
  8. Sahoo P K, Soltani S., 1988. A svrvey of thresholding techniques. Comput. Vision Graphics and Image Process.
  9. Kennedy J, Eberhart R. C., 1995. Particle Swarm Optimization. In: Proc. IEEE Int'l. Conf. on Neural Networks, IV. Piscataway, NJ: IEEE Service Center Carlisle A, Dozier G., 2001. An Off-the-shelf PSO. In Proceedings of the Workshop on Particle Swarm Optimization. Indianapolis, IN: Purdue School of Engineering and Technology, IUPUI(in press).
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Paper Citation


in Harvard Style

Zheng L., Li G., Liang J. and Pan Q. (2009). IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BASED ON PSO . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 287-291. DOI: 10.5220/0002314302870291


in Bibtex Style

@conference{icec09,
author={Liping Zheng and Guangyao Li and Jing Liang and Quanke Pan},
title={IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BASED ON PSO },
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)},
year={2009},
pages={287-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002314302870291},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)
TI - IMPROVED 2D MAXIMUM ENTROPY THRESHOLD SEGMENTATION METHOD BASED ON PSO
SN - 978-989-674-014-6
AU - Zheng L.
AU - Li G.
AU - Liang J.
AU - Pan Q.
PY - 2009
SP - 287
EP - 291
DO - 10.5220/0002314302870291