Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.650
JCR 5-Year IF: 0.639
Issues per year: 4
Current issue: Nov 2019
Next issue: Feb 2020
Avg review time: 71 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,499,169 unique visits
640,852 downloads
Since November 1, 2009



No robots online now


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 19 (2019)
 
     »   Issue 4 / 2019
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
  View all issues  








LATEST NEWS

2019-Dec-16
Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Read More »


    
 

  1/2018 - 14

A Novel Approach for Bi-Level Segmentation of Tuberculosis Bacilli Based on Meta-Heuristic Algorithms

AYAS, S. See more information about AYAS, S. on SCOPUS See more information about AYAS, S. on IEEExplore See more information about AYAS, S. on Web of Science, DOGAN, H. See more information about  DOGAN, H. on SCOPUS See more information about  DOGAN, H. on SCOPUS See more information about DOGAN, H. on Web of Science, GEDIKLI, E. See more information about  GEDIKLI, E. on SCOPUS See more information about  GEDIKLI, E. on SCOPUS See more information about GEDIKLI, E. on Web of Science, EKINCI, M. See more information about EKINCI, M. on SCOPUS See more information about EKINCI, M. on SCOPUS See more information about EKINCI, M. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,407 KB) | Citation | Downloads: 302 | Views: 2,489

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
Quick view
Full text preview
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

[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 79] [SCOPUS Times Cited 114]


[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 2175] [SCOPUS Times Cited 2968]


[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 19]


[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 29]


[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 5]


[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 22] [SCOPUS Times Cited 50]


[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 44] [SCOPUS Times Cited 73]


[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 11]


[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 109] [SCOPUS Times Cited 135]


[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 9]


[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 40] [SCOPUS Times Cited 58]


[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 3] [SCOPUS Times Cited 8]


[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 41] [SCOPUS Times Cited 55]


[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 22] [SCOPUS Times Cited 23]


[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 5]


[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 9]


[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 41] [SCOPUS Times Cited 65]


[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 3613]


[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 389]


[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 1451] [SCOPUS Times Cited 1914]


[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 12] [SCOPUS Times Cited 23]


[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 1843]


[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 163] [SCOPUS Times Cited 211]


[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] [SCOPUS Times Cited 3163]


[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 1369]


[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 780]


[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.



References Weight

Web of Science® Citations for all references: 4,221 TCR
SCOPUS® Citations for all references: 16,922 TCR

Web of Science® Average Citations per reference: 111 ACR
SCOPUS® Average Citations per reference: 445 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 2020-02-22 10:02 in 201 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2020
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.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

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.




Website loading speed and performance optimization powered by: