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A Novel Approach for Bi-Level Segmentation of Tuberculosis Bacilli Based on Meta-Heuristic AlgorithmsAYAS, S.   , DOGAN, H. , GEDIKLI, E. , EKINCI, M. |
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Author keywords
computer aided analysis, heuristic algorithms, image segmentation, information entropy, particle swarm optimization
References keywords
tuberculosis(12), thresholding(10), segmentation(10), image(10), images(8), algorithm(7), algorithms(6), yang(5), sputum(5), method(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 113 - 120
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01014
Web of Science Accession Number: 000426449500014
SCOPUS ID: 85043229405
Abstract
Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms. |
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| [1] World Health Organization (WHO), Global tuberculosis control: WHO report, pp. 35-36, 2017.
[2] J. C. Palomino, S. C. Leao, V. Ritacco, "Tuberculosis 2007; from basic science to patient care", pp. 95-97, 2007. [3] A. Laszlo, "Sputum examination for tuberculosis by direct microscopy in low income countries", IUATLD Technical Guide. International Union against Tuberculosis and Lung Disease, Paris, France, pp. 7-15, 2000. [4] C. T. Division, "Module for Laboratory Technicians", Module for Laboratory Technicians (Central TB Division, 2005), pp. 26-27, 2005. [5] M. G. Forero, F. Sroubek, G. Cristobal, "Identification of tuberculosis bacteria based on shape and color", Real-time imaging 10(4), pp. 251-262, 2004. [CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 137] [6] M. Sezgin, B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic imaging 13(1), pp. 146-168, 2004. [CrossRef] [Web of Science Times Cited 3001] [SCOPUS Times Cited 3955] [7] M. G. Costa, C. F. Costa Filho, J. F. Sena, J. Salem, M. O. de Lima, "Automatic identification of mycobacterium tuberculosis with conventional light microscopy", in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (IEEE, 2008), pp. 382-385. [CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 53] [8] P. Sadaphal, J. Rao, G. Comstock, M. Beg, "Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains [Short Communication]", The International Journal of Tuberculosis and Lung Disease 12(5), pp. 579-582, 2008. [9] R. Raof, Z. Salleh, S. Sahidan, M. Mashor, S. M. Noor, F. M. Idris, H. Hasan, "Color thresholding method for image segmentation algorithm of Ziehl-Neelsen sputum slide images", in Electrical Engineering, Computing Science and Automatic Control, 2008. CCE 2008. 5th International Conference on (IEEE, 2008), pp. 212-217. [CrossRef] [SCOPUS Times Cited 40] [10] R. A. A. Raof, M. Y. Mashor, R. B. Ahmad, S. S. M. Noor, M. K. Osman, "Comparison of colour thresholding method using RGB and HSI information for Ziehl-Neelsen sputum slide images", in Information Sciences Signal Processing and their Applications (ISSPA), 2010 10th International Conference on (IEEE, 2010), pp. 724-727. [CrossRef] [SCOPUS Times Cited 7] [11] V. Makkapati, R. Agrawal, R. Acharya, "Segmentation and classification of tuberculosis bacilli from ZN-stained sputum smear images", in Automation Science and Engineering, 2009.CASE 2009. IEEE International Conference on (IEEE, 2009), pp. 217-220. [CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 72] [12] R. Khutlang, S. Krishnan, R. Dendere, A. Whitelaw, K. Veropoulos, G. Learmonth, T.S. Douglas, "Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears", Information Technology in Biomedicine, IEEE Transactions on 14(4), pp. 949-957, 2010. [CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 104] [13] M. Osman, M. Mashor, H. Jaafar, "Performance comparison of clustering and thresholding algorithms for tuberculosis bacilli segmentation", in Computer, Information and Telecommunication Systems (CITS), 2012 International Conference on (IEEE, 2012), pp. 1-5. [CrossRef] [SCOPUS Times Cited 20] [14] K. Hammouche, M. Diaf, P. Siarry, "A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem", Engineering Applications of Artificial Intelligence 23(5), pp. 676-688, 2010. [CrossRef] [Web of Science Times Cited 155] [SCOPUS Times Cited 201] [15] S. Bejinariu, H. Costin, F. Rotaru, R. Luca, C. D. Nita, "Automatic multi-threshold image segmentation using metaheuristic algorithms", in Signals, Circuits and Systems (ISSCS), 2015 International Symposium on (IEEE, 2015), pp. 1-4. [CrossRef] [SCOPUS Times Cited 12] [16] M. Maitra, A. Chatterjee, "A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging", Measurement 41(10), pp. 1124-1134, 2008. [CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 77] [17] J. A. A. Jothi, V. M. A. Rajam, "Segmentation of Nuclei from Breast Histopathology Images Using PSO-based Otsu's Multilevel Thresholding", in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (Springer, 2015), pp. 835-843. [CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 15] [18] P. Sathya, R. Kayalvizhi, "Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm", Neurocomputing 74(14), pp. 2299-2313, 2011. [CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 90] [19] A. Bouaziz, A. Draa, S. Chikhi, "Artificial bees for multilevel thresholding of iris images", Swarm and Evolutionary Computation 21, pp. 32-40, 2015. [CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 33] [20] S. Ayas, H. Dogan, E. Gedikli, M. Ekinci, "Microscopic image segmentation based on firefly algorithm for detection of tuberculosis bacteria", in Signal Processing and Communications Applications Conference (SIU), 2015 23th (IEEE, 2015), pp. 851-854. [CrossRef] [SCOPUS Times Cited 12] [21] P. Filipczuk, W. Wojtak, A. Obuchowicz, "Automatic nuclei detection on cytological images using the firefly optimization algorithm", in Information Technologies in Biomedicine (Springer, 2012), pp. 85-92. [CrossRef] [SCOPUS Times Cited 11] [22] J. MacQueen, "Some methods for classification and analysis of multivariate observations", in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1 (Oakland, CA, USA., 1967), vol. 1, pp. 281-297. [23] H. Yao, Q. Duan, D. Li, J. Wang, "An improved K-means clustering algorithm for fish image segmentation", Mathematical and Computer Modelling 58(3), pp. 790-798, 2013. [CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 129] [24] J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters", Taylor & Francis, pp. 32-57, 1973. [CrossRef] [SCOPUS Times Cited 5040] [25] Y. W. Lim, S. U. Lee, "On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques", Pattern recognition 23(9), pp. 935-952, 1990. [CrossRef] [SCOPUS Times Cited 434] [26] J. A. Sethian, "A fast marching level set method for monotonically advancing fronts", Proceedings of the National Academy of Sciences 93(4), pp. 1591-1595, 1996. [CrossRef] [Web of Science Times Cited 2037] [SCOPUS Times Cited 2642] [27] P. Campadelli, E. Casiraghi, S. Pratissoli, "Fully automatic segmentation of abdominal organs from CT images using fast marching methods", in Computer-Based Medical Systems, 2008. CBMS'08. 21st IEEE International Symposium on (IEEE, 2008), pp. 554-559. [CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 25] [28] X. S. Yang, "Nature-inspired metaheuristic algorithms", Luniver press, pp. 4-5, 2010. [29] X. S. Yang, "Firefly algorithms for multimodal optimization", in Stochastic algorithms foundations and applications (Springer, 2009), pp.169-178. [CrossRef] [SCOPUS Times Cited 3545] [30] R. C. Eberhart, J. Kennedy, "A new optimizer using particle swarm theory", in Proceedings of the sixth international symposium on micro machine and human science, vol. 1 (New York, NY, 1995), vol. 1, pp. 39-43. [CrossRef] [31] M. Omran, A. P. Engelbrecht, A. Salman, "Particle swarm optimization method for image clustering", International Journal of Pattern Recognition and Artificial Intelligence 19(03), pp. 297-321, 2005. [CrossRef] [Web of Science Times Cited 202] [SCOPUS Times Cited 266] [32] X. S. Yang, S. Deb, "Cuckoo search via Levy flights", in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (IEEE, 2009), pp. 210-214. [CrossRef] [Web of Science Times Cited 4786] [SCOPUS Times Cited 6488] [33] X. S. Yang, S. Deb, "Engineering optimisation by cuckoo search", International Journal of Mathematical Modelling and Numerical Optimisation 1(4), pp. 330-343, 2010. [CrossRef] [SCOPUS Times Cited 2173] [34] X. S. Yang, "Flower pollination algorithm for global optimization", in Unconventional computation and natural computation (Springer, 2012), pp. 240-249. [CrossRef] [SCOPUS Times Cited 1985] [35] J. N. Kapur, P. K. Sahoo, A. K. Wong, "A new method for gray-level picture thresholding using the entropy of the histogram", Computer vision, graphics, and image processing 29(3), pp. 273-285, 1985. [CrossRef] [36] CVPR. Computer vision and pattern recognition laboratory. http://ceng2.ktu.edu.tr/~cvpr/ , 2013. [37] W. Zhu, N. Zeng, N. Wang, "Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations", in Proceedings of the Northeast SAS users group conference (NESUG10), 2010. Web of Science® Citations for all references: 10,677 TCR SCOPUS® Citations for all references: 27,566 TCR Web of Science® Average Citations per reference: 281 ACR SCOPUS® Average Citations per reference: 725 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-11-27 05:26 in 191 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
