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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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computer aided analysis, heuristic algorithms, image segmentation, information entropy, particle swarm optimization
tuberculosis(12), thresholding(10), segmentation(10), image(10), images(8), algorithm(7), algorithms(6), yang(5), sputum(5), method(5)
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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
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.
|References|||||Cited By «-- Click to see who has cited this paper|
| World Health Organization (WHO), Global tuberculosis control: WHO report, pp. 35-36, 2017.
 J. C. Palomino, S. C. Leao, V. Ritacco, "Tuberculosis 2007; from basic science to patient care", pp. 95-97, 2007.
 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.
 C. T. Division, "Module for Laboratory Technicians", Module for Laboratory Technicians (Central TB Division, 2005), pp. 26-27, 2005.
 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] [SCOPUS Times Cited 104]
 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 1887] [SCOPUS Times Cited 2590]
 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.
 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.
 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 24]
 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 3]
 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] [SCOPUS Times Cited 42]
 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] [SCOPUS Times Cited 64]
 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 10]
 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 83] [SCOPUS Times Cited 107]
 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 6]
 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] [SCOPUS Times Cited 45]
 J. A. A. Jothi, V. M. A. Rajam, "Segmentation of Nuclei from Breast Histopathology Images Using PSO-based Otsus Multilevel Thresholding", in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (Springer, 2015), pp. 835-843.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 5]
 P. Sathya, R. Kayalvizhi, "Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm", Neurocomputing 74(14), pp. 2299-2313, 2011.
[CrossRef] [SCOPUS Times Cited 40]
 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 13] [SCOPUS Times Cited 14]
 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 3]
 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 9]
 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.
 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 34] [SCOPUS Times Cited 46]
 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 3153]
 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 369]
 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] [SCOPUS Times Cited 1701]
 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] [SCOPUS Times Cited 19]
 X. S. Yang, "Nature-inspired metaheuristic algorithms", Luniver press, pp. 4-5, 2010.
 X. S. Yang, "Firefly algorithms for multimodal optimization", in Stochastic algorithms foundations and applications (Springer, 2009), pp.169-178.
[CrossRef] [SCOPUS Times Cited 1358]
 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.
 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 139] [SCOPUS Times Cited 178]
 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] [SCOPUS Times Cited 2226]
 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 1090]
 X. S. Yang, "Flower pollination algorithm for global optimization", in Unconventional computation and natural computation (Springer, 2012), pp. 240-249.
[CrossRef] [SCOPUS Times Cited 436]
 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.
 CVPR. Computer vision and pattern recognition laboratory. http://ceng2.ktu.edu.tr/~cvpr/ , 2013.
 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.
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