CFP last date
20 March 2024
Call for Paper
April Edition
IJCA solicits high quality original research papers for the upcoming April edition of the journal. The last date of research paper submission is 20 March 2024

Submit your paper
Know more
Reseach Article

Optimal Multilevel Threshold Selection for Gray Level Image Segmentation using SMS Algorithm

by Kotte Sowjanya, P. Rajesh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 11
Year of Publication: 2017
Authors: Kotte Sowjanya, P. Rajesh Kumar
10.5120/ijca2017913778

Kotte Sowjanya, P. Rajesh Kumar . Optimal Multilevel Threshold Selection for Gray Level Image Segmentation using SMS Algorithm. International Journal of Computer Applications. 163, 11 ( Apr 2017), 35-47. DOI=10.5120/ijca2017913778

@article{ 10.5120/ijca2017913778,
author = { Kotte Sowjanya, P. Rajesh Kumar },
title = { Optimal Multilevel Threshold Selection for Gray Level Image Segmentation using SMS Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 11 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 35-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number11/27442-2017913778/ },
doi = { 10.5120/ijca2017913778 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:58.002739+05:30
%A Kotte Sowjanya
%A P. Rajesh Kumar
%T Optimal Multilevel Threshold Selection for Gray Level Image Segmentation using SMS Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 11
%P 35-47
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing is one of the real research regions in the most recent four decades. Numerous researchers have contributed very great algorithms and reported outstanding results. In this paper, state of matter search optimization based multilevel thresholding is implemented for the segmentation of gray scale Images. Set of standard gray level images are considered for image segmentation. The optimal multilevel threshold is found by maximizing the very popular objectives such as between class variance (Otsu method) and Kapur’s entropy. The outcomes are looked at with the aftereffects of the existing algorithms like IDSA, HSA, PSO, and BF. The outcomes uncover that the execution of state of matter search optimization algorithm based optimal multilevel threshold for image segmentation is better and has predictable execution than officially reported techniques.

References
  1. S. Patra, R. Gautam, A. Singla, A novel context sensitive multilevel thresholding for image segmentation, Appl. Soft Comput. J. 23 (2014) 122–127. doi:10.1016/j.asoc.2014.06.016.
  2. D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, M. Perez-Cisneros, Multilevel thresholding segmentation based on harmony search optimization, J. Appl. Math. 2013 (2013). doi:10.1155/2013/575414.
  3. P. Smith, D.B. Reid, C. Environment, L. Palo, P. Alto, P.L. Smith, A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man. Cybern. 9 (1979) 62–66. doi:10.1109/TSMC.1979.4310076.
  4. J.N. Kapur, P.K. Sahoo, a. K.C. Wong, A new method for gray-level picture thresholding using the entropy of the histogram, Comput. Vision, Graph. Image Process. 29 (1985) 140. doi:10.1016/S0734-189X(85)90156-2.
  5. J. Kittler, J. Illingworth, Minimum error thresholding, Pattern Recognit. 19 (1986) 41–47. doi:10.1016/0031-3203(86)90030-0.
  6. N.R. Pal, S.K. Pal, A review on image segmentation techniques, Pattern Recognit. 26 (1993) 1277–1294. doi:10.1016/0031-3203(93)90135-J
  7. P.. Sahoo, S. Soltani, a. K.. Wong, A survey of thresholding techniques, Comput. Vision, Graph. Image Process. 41 (1988) 233–260. doi:10.1016/0734-189X(88)90022-9.
  8. C.-C. Chang, L.-L. Wang, A fast multilevel thresholding method based on lowpass and highpass filtering, Pattern Recognit. Lett. 18 (1997) 1469–1478. doi:10.1016/S0167-8655(97)00134-7.
  9. Q. Huang, W. Gao, W. Cai, Thresholding technique with adaptive window selection for uneven lighting image, Pattern Recognit. Lett. 26 (2005) 801–808. doi:10.1016/j.patrec.2004.09.035.
  10. P. Smith, D.B. Reid, C. Environment, L. Palo, P. Alto, P.L. Smith, Smith et al. - 1979 - A Tlreshold Selection Method from Gray-Level Histograms, 20 (1979) 62–66. doi:10.1109/TSMC.1979.4310076.
  11. S. Cho, R. Haralick, S. Yi, Improvement of kittler and illingworth’s minimum error thresholding, Pattern Recognit. 22 (1989) 609–617. doi:10.1016/0031-3203(89)90029-0.
  12. C.-C. Lai, D.-C. Tseng, A Hybrid Approach Using Gaussian Smoothing and Genetic Algorithm for Multilevel Thresholding, Int. J. Hybrid Intell. Syst. 1 (2004) 143–152. http://content.iospress.com/articles/international-journal-of-hybrid-intelligent-systems/his015.
  13. P.-Y. Yin, A fast scheme for optimal thresholding using genetic algorithms, Signal Processing. 72 (1999) 85–95. doi:10.1016/S0165-1684(98)00167-4.
  14. J. Kennedy, R. Eberhart, Particle swarm optimization, Neural Networks, 1995. Proceedings., IEEE Int. Conf. 4 (1995) 1942–1948 vol.4. doi:10.1109/ICNN.1995.488968.
  15. D. Karaboga, An idea based on Honey Bee Swarm for Numerical Optimization, Tech. Rep. TR06, Erciyes Univ. (2005) 10. doi:citeulike-article-id:6592152.
  16. B. Akay, A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding, Appl. Soft Comput. 13 (2012) 3066–3091. doi:10.1016/j.asoc.2012.03.072.
  17. P.D. Sathya, R. Kayalvizhi, Optimal multilevel thresholding using bacterial foraging algorithm, Expert Syst. Appl. 38 (2011) 15549–15564. doi:10.1016/j.eswa.2011.06.004.
  18. P.D. Sathya, R. Kayalvizhi, Modified bacterial foraging algorithm based multilevel thresholding for image segmentation, Eng. Appl. Artif. Intell. 24 (2011) 595–615. doi:10.1016/j.engappai.2010.12.001.
  19. A.K. Bhandari, V.K. Singh, A. Kumar, G.K. Singh, Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy, Expert Syst. Appl. 41 (2014) 3538–3560. doi:10.1016/j.eswa.2013.10.059.
  20. T. Kurban, P. Civicioglu, R. Kurban, E. Besdok, Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding, Appl. Soft Comput. J. 23 (2014) 128–143. doi:10.1016/j.asoc.2014.05.037.
  21. S. Kotte, P.R. Kumar, S. Kumar, An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm, Ain Shams Eng. J. (2016). doi:10.1016/j.asej.2016.06.007.
  22. E. Cuevas, An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation, 40 (2014) 256–272.
Index Terms

Computer Science
Information Sciences

Keywords

Multilevel thresholding gray scale image segmentation state of matter search optimization qualitative and quantitative analysis