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Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm

IJCA Proceedings on International Conference on Communication, Computing and Information Technology
© 2015 by IJCA Journal
ICCCMIT 2014 - Number 3
Year of Publication: 2015
B. Joyce Preethi
R. Angel Sujitha
V. Rajinikanth

Joyce B Preethi, Angel R Sujitha and V Rajinikanth. Article: Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm. IJCA Proceedings on International Conference on Communication, Computing and Information Technology ICCCMIT 2014(3):10-16, March 2015. Full text available. BibTeX

	author = {B. Joyce Preethi and R. Angel Sujitha and V. Rajinikanth},
	title = {Article: Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm},
	journal = {IJCA Proceedings on International Conference on Communication, Computing and Information Technology},
	year = {2015},
	volume = {ICCCMIT 2014},
	number = {3},
	pages = {10-16},
	month = {March},
	note = {Full text available}


In this work, bi-level and multi-level segmentation is proposed for the grey image dataset using a novel Brownian Bat Algorithm (BBA). Maximization of Otsu's between-class variance function is chosen as the objective function. The performance of the proposed CBA is demonstrated by considering five benchmark images and compared with the existing bat algorithms such as Traditional Bat Algorithm (TBA) and the Lévy flight Bat Algorithm (LBA). The performance appraisal between the proposed and existing bat algorithms are done using existing constraints such as objective function, Root Mean Squared Error (RMSE), Peak to Signal Ratio (PSNR), Structural Dissimilarity (DSSIM) index, and algorithm convergence. The result evident that proposed CBA offers better values for objective function, RMSE, PSNR and DSSIM, whereas TBA and LBA offers faster convergence compared to BBA.


  • Sezgin, M and Sankar, B. 2004. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation, Journal of Electronic Imaging. 13(1), 146 – 165.
  • Lee, S. U. , Chung, S. Y. and Park, R. H. 1990. A Comparative Performance Study Techniques for Segmentation, Computer Vision, Graphics and Image Processing,, 52(2), 171 – 190 .
  • Pal, N. R. and Pal, S. K . 1993. A review on image segmentation techniques, Pattern Recognition, 26(9), 1277 – 1294.
  • Sathya, P. D. and Kayalvizhi, R. 2011. Modified bacterial foraging algorithm based multilevel thresholding for image segmentation, Engineering Applications of Artificial Intelligence, 24, 595–615.
  • Sathya, P. D. and Kayalvizhi, R. 2011. Optimal multilevel thresholding using bacterial foraging algorithm, Expert Systems with Applications, 38, 15549–15564.
  • Otsu, N . 1979. A Threshold selection method from Gray-Level Histograms, IEEE T. on Systems, Man and Cybernetics. 9(1), 62-66.
  • Akay, B. 2013. A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding,Applied Soft Computing. 13(6),3066–3091.
  • Charansiriphaisan, K. , Chiewchanwattana, S. and Sunat, K. 2013. A comparative study of improved artificial bee colony algorithms applied to multilevel image thresholding, Mathematical Problems in Engineering, , Article ID 927591, 17 pages.
  • Ghamisi, P. , Couceiro, M. S. , Benediktsson, J. A. and Ferreira, N. M. F. 2012 . An efficient method for segmentation of images based on fractional calculus and natural selection, Expert Syst. Appl. , 39(16), 12407– 12417.
  • Rajinikanth, V. , Sri Madhava Raja, N. and Latha, K. 2014. Optimal Multilevel Image Thresholding: An Analysis with PSO and BFO Algorithms, Aust. J. Basic & Appl. Sci. , 8 (9), 443-454.
  • Ghamisi, P. , Couceiro, M. S. and Benediktsson, B. A. 2013. Classification of hyperspectral images with binary fractional order Darwinian PSO and random forests, SPIE Remote Sensing, 88920S-88920S-8.
  • Ghamisi, P. , Couceiro, M. S. , Martins, F. M. L. and Benediktsson, J. A . 2014. Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization, IEEE T. on Geoscience and Remote sensing, 52(5), 2382-2394.
  • Yang, X. S. 2010 . A new metaheuristic bat-inspired algorithm, In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Eds. Cruz C. , Gonzalez J. , Krasnogor N. , and Terraza G. ), Springer, SCI 284, 65-74.
  • Raja, N. S. M. , Rajinikanth, V. and Latha, K. 2014. Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm, Modelling and Simulation in Engineering, Article ID 794574, 17 pages.
  • Yang, X. S. 2013. Bat algorithm: literature review and applications, Int. J. Bio-Inspired Computation, 5( 3), 141–149.
  • Yang, X. S. and Gandomi, A. H. 2012. Bat Algorithm: A Novel Approach for Global Engineering Optimization, Engineering Computations,29(5), 464—483.
  • Yang, X. S. 2008 . Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, UK.
  • Alihodzic, A. and Tuba, M. 2014. Improved Bat Algorithm Applied to Multilevel Image Thresholding, The Scientific World Journal, vol. 2014, Article ID 176718, 16 pages.
  • Kotteeswaran, R. and Sivakumar, L. 2013. A Novel Bat Algorithm Based Re-tuning of PI Controller of Coal Gasifier for Optimum Response,In R. Prasath and T. Kathirvalavakumar (Eds. ): MIKE 2013, LNAI 8284, 506-517.
  • Metzler, R. and Klafter, J. 2000. The random walk's guide to anomalous diffusion: a fractional dynamics approach, Physics Reports, 339(1), 1-77.
  • Nurzaman, S. G. , Matsumoto, Y. , Nakamura, Y. , Shirai, K. and Koizumi,S. 2011. From Lévy to Brownian: A Computational Model Based on Biological Fluctuation, PLoS ONE, 6(2), e16168, 2011. DOI:10. 1371/journal. pone. 0016168.
  • Rajinikanth, V. , Aashiha, J. P. and Atchaya, A. 2014, Gray-Level Histogram based Multilevel Threshold Selection with Bat Algorithm, International Journal of Computer Applications, 93(16), 1-8.
  • Lin, J. H. , Chou, C. W. , Yang, C. H. and Tsai, H. L. 2012 . A chaotic Levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems, J. Computer and Information Technology, 2(2), 56–63.