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Otsu Based Optimal Multilevel Image Thresholding Using Coronavirus Herd Immunity Optimizer

Year 2023, Volume: 16 Issue: 1, 1 - 11, 31.01.2023
https://doi.org/10.17671/gazibtd.1172909

Abstract

Thresholding selection plays an important role in image segmentation. Minimum error method, iterative method, entropy method and Otsu method are known as the most useful methods for thresholding selection. In this study, Otsu technique is used as thresholding method. Since the complexity of the problem would increase exponentially depending on the increase in the number of thresholds (K), it seems more appropriate to use a swarm intelligence algorithm instead of mathematical methods. Therefore, in this study, the Coronavirus herd immunity optimizer (CHIO), which has been introduced to the literature in recent years, is used as a swarm intelligence algorithm. In the experimental studies, six different images are used as test data in experimental studies. K value is determined as 2, 3, 4 and 5 in this study. Using this data set, the CHIO algorithm is compared with the successful algorithms in the literature such as differential evolution (DE), gray wolf (GWO), and particle swarm (PSO) algorithms in the equal conditions. According to the results obtained, it is seen that in studies conducted on 6 test data using the CHIO algorithm, the proposed algorithm achieves the best results in 100% of the images when K=2, 83% when K=3 and 4, and finally 50% when K=5. In the light of these results, it has been determined that the CHIO algorithm is competitive in terms of solution quality. As a result, the CHIO algorithm can be an alternative algorithm for the multilevel image thresholding problem.

References

  • M. Omari and S. O. Jaafri, "Application of image compression to multiple-shot pictures using similarity norms with three level blurring”, Computers, Materials and Continua, 58(2), 753-775, 2019.
  • Z. Pan, X. Yi, Y. Zhang, B. Jeon, and S. Kwong, "Efficient in-loop filtering based on enhanced deep convolutional neural networks for HEVC”, IEEE Transactions on Image Processing, 29, 5352-5366, 2020.
  • K. Jin and S. Wang, "Image denoising based on the asymmetric Gaussian mixture model”, J. Internet Things, 2(1), 1-11, 2020.
  • S. Susan and K. Rachna Devi, "Text area segmentation from document images by novel adaptive thresholding and template matching using texture cues”, Pattern Analysis and Applications, 23(2), 869-881, 2020.
  • S. Bandyopadhyay, S. Das, and A. Datta, "A hybrid fuzzy filtering-fuzzy thresholding technique for region of interest detection in noisy images”, Applied Intelligence, 50(4), 1112-1132, 2020.
  • K. Sowjanya and S. K. Injeti, "Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding”, Expert Systems with Applications, 182, 115286, 2021.
  • B. Akay, "A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding”, Applied Soft Computing, 13(6), 3066-3091, 2013.
  • D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, and M. Perez-Cisneros, "Multilevel thresholding segmentation based on harmony search optimization”, Journal of Applied Mathematics, 2013, 2013.
  • J. N. Kapur, P. K. Sahoo, and 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), 273-285, 1985.
  • S. Pare, A. Kumar, G. K. Singh, and V. Bajaj, "Image segmentation using multilevel thresholding: a research review”, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 1-29, 2020.
  • L. Li, L. Sun, Y. Xue, S. Li, X. Huang, and R. F. Mansour, "Fuzzy multilevel image thresholding based on improved coyote optimization algorithm”, IEEE Access, 9, 33595-33607, 2021.
  • N. Sri Madhava Raja, V. Rajinikanth, and K. Latha, "Otsu based optimal multilevel image thresholding using firefly algorithm”, Modelling and Simulation in Engineering, 2014, 2014.
  • M. Abd El Aziz, A. A. Ewees, and A. E. Hassanien, "Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation”, Expert Systems with Applications, 83, 242-256, 2017.
  • S. Kotte, R. K. Pullakura, and S. K. Injeti, "Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization”, Measurement, 130, 340-361, 2018.
  • A. S. Kahraman, T. R. Farshi, and R. Demirci, "Renkli görüntülerin çok seviyeli eşiklenmesi ve sınıflandırılması”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(4), 846-859, 2018.
  • I. Koc, O. K. Baykan, and I. Babaoglu, "Gri kurt optimizasyon algoritmasına dayanan çok seviyeli imge eşik seçimi”, Politeknik Dergisi, 21(4), 841-847, 2018.
  • M. Abd Elaziz and S. Lu, "Many-objectives multilevel thresholding image segmentation using knee evolutionary algorithm”, Expert systems with Applications, 125, 305-316, 2019.
  • A. Çelik and S. Demirel, "Otsu ve Ridler-Calvard Görüntü İşleme Yöntemlerinin Zatürre Tespitinde Kullanılması”, Muş Alparslan Üniversitesi Fen Bilimleri Dergisi, 10(1), 917-923.
  • B. Karasulu, "Görüntülerde insan kulağı tespit ve bölütlemesini temel alan biyometrik yetkilendirme üzerine bir inceleme”, Bilişim Teknolojileri Dergisi, 9(2), 97, 2016.
  • Y. Ölmez, A. Sengur, and G. Ozmen Koca, "Multilevel thresholding with metaheuristic methods”, Journal of the Faculty of Engineering and Architecture of Gazi University, 36(1), 213-224, 2020.
  • N. Otsu, "A threshold selection method from gray-level histograms”, IEEE transactions on systems, man, and cybernetics, 9(1), 62-66, 1979.
  • M. A. Al-Betar, Z. A. A. Alyasseri, M. A. Awadallah, and I. Abu Doush, "Coronavirus herd immunity optimizer (CHIO)”, Neural Computing and Applications, 33(10), 5011-5042, 2021.
  • J. S. Lavine, A. A. King, and O. N. Bjørnstad, "Natural immune boosting in pertussis dynamics and the potential for long-term vaccine failure”, Proceedings of the National Academy of Sciences, 108(17), 7259-7264, 2011.

Coronavirüs Sürü Bağışıklığı Algoritması ile Otsu Tabanlı Optimal Çok Düzeyli Görüntü Eşiği

Year 2023, Volume: 16 Issue: 1, 1 - 11, 31.01.2023
https://doi.org/10.17671/gazibtd.1172909

Abstract

Eşik seçimi, görüntü bölütlemede önemli bir rol oynamaktadır. Eşik seçimiyle ilgili en faydalı yöntemler olarak minimum hata yöntemi, iteratif yöntem, entropi yöntemi ve Otsu yöntemi bilinmektedir. Bu çalışmada eşikleme yöntemi olarak Otsu tekniği kullanılmaktadır. Eşik sayısının (K) artmasına bağlı olarak problemin karmaşıklık düzeyi üstel olarak artacağı için matematiksel yöntemler yerine sürü zekâsı algoritması kullanılması daha uygun görülmektedir. Bundan dolayı, bu çalışmada sürü zekâsı algoritması olarak da son yıllarda literatüre kazandırılmış olan Coronavirüs sürü bağışıklığı algoritması (CHIO) kullanılmaktadır. Deneysel çalışmalarda test verisi olarak altı farklı görüntü kullanılmaktadır. K değeri bu çalışmada 2, 3, 4 ve 5 olarak belirlenmektedir. Bu veri seti kullanılarak CHIO algoritması ile literatürde yer alan diferansiyel evrim (differential evolution: DE), gri kurt ( gray wolf optimizer: GWO), parçacık sürü (particle swarm optimization: PSO) algoritmaları gibi başarılı algoritmalarla eşit koşullarda kıyaslanmaktadır. Elde edilen sonuçlara göre, CHIO algoritması kullanılarak 6 test verisi üzerinde yapılan çalışmalarda K=2 olduğunda verilerin %100, K=3 ve 4 iken %83 ve son olarak K=5 iken %50’sinde en iyi sonuçları yakaladığı görülmektedir. Bu sonuçlar ışığında, CHIO algoritmasının çözüm kalitesi açısından rekabet edici olduğu tespit edilmiştir. Sonuç olarak CHIO algoritması çok düzeyli görüntü eşiği problemi için alternatif bir algoritma olabilir.

References

  • M. Omari and S. O. Jaafri, "Application of image compression to multiple-shot pictures using similarity norms with three level blurring”, Computers, Materials and Continua, 58(2), 753-775, 2019.
  • Z. Pan, X. Yi, Y. Zhang, B. Jeon, and S. Kwong, "Efficient in-loop filtering based on enhanced deep convolutional neural networks for HEVC”, IEEE Transactions on Image Processing, 29, 5352-5366, 2020.
  • K. Jin and S. Wang, "Image denoising based on the asymmetric Gaussian mixture model”, J. Internet Things, 2(1), 1-11, 2020.
  • S. Susan and K. Rachna Devi, "Text area segmentation from document images by novel adaptive thresholding and template matching using texture cues”, Pattern Analysis and Applications, 23(2), 869-881, 2020.
  • S. Bandyopadhyay, S. Das, and A. Datta, "A hybrid fuzzy filtering-fuzzy thresholding technique for region of interest detection in noisy images”, Applied Intelligence, 50(4), 1112-1132, 2020.
  • K. Sowjanya and S. K. Injeti, "Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding”, Expert Systems with Applications, 182, 115286, 2021.
  • B. Akay, "A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding”, Applied Soft Computing, 13(6), 3066-3091, 2013.
  • D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, and M. Perez-Cisneros, "Multilevel thresholding segmentation based on harmony search optimization”, Journal of Applied Mathematics, 2013, 2013.
  • J. N. Kapur, P. K. Sahoo, and 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), 273-285, 1985.
  • S. Pare, A. Kumar, G. K. Singh, and V. Bajaj, "Image segmentation using multilevel thresholding: a research review”, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 1-29, 2020.
  • L. Li, L. Sun, Y. Xue, S. Li, X. Huang, and R. F. Mansour, "Fuzzy multilevel image thresholding based on improved coyote optimization algorithm”, IEEE Access, 9, 33595-33607, 2021.
  • N. Sri Madhava Raja, V. Rajinikanth, and K. Latha, "Otsu based optimal multilevel image thresholding using firefly algorithm”, Modelling and Simulation in Engineering, 2014, 2014.
  • M. Abd El Aziz, A. A. Ewees, and A. E. Hassanien, "Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation”, Expert Systems with Applications, 83, 242-256, 2017.
  • S. Kotte, R. K. Pullakura, and S. K. Injeti, "Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization”, Measurement, 130, 340-361, 2018.
  • A. S. Kahraman, T. R. Farshi, and R. Demirci, "Renkli görüntülerin çok seviyeli eşiklenmesi ve sınıflandırılması”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6(4), 846-859, 2018.
  • I. Koc, O. K. Baykan, and I. Babaoglu, "Gri kurt optimizasyon algoritmasına dayanan çok seviyeli imge eşik seçimi”, Politeknik Dergisi, 21(4), 841-847, 2018.
  • M. Abd Elaziz and S. Lu, "Many-objectives multilevel thresholding image segmentation using knee evolutionary algorithm”, Expert systems with Applications, 125, 305-316, 2019.
  • A. Çelik and S. Demirel, "Otsu ve Ridler-Calvard Görüntü İşleme Yöntemlerinin Zatürre Tespitinde Kullanılması”, Muş Alparslan Üniversitesi Fen Bilimleri Dergisi, 10(1), 917-923.
  • B. Karasulu, "Görüntülerde insan kulağı tespit ve bölütlemesini temel alan biyometrik yetkilendirme üzerine bir inceleme”, Bilişim Teknolojileri Dergisi, 9(2), 97, 2016.
  • Y. Ölmez, A. Sengur, and G. Ozmen Koca, "Multilevel thresholding with metaheuristic methods”, Journal of the Faculty of Engineering and Architecture of Gazi University, 36(1), 213-224, 2020.
  • N. Otsu, "A threshold selection method from gray-level histograms”, IEEE transactions on systems, man, and cybernetics, 9(1), 62-66, 1979.
  • M. A. Al-Betar, Z. A. A. Alyasseri, M. A. Awadallah, and I. Abu Doush, "Coronavirus herd immunity optimizer (CHIO)”, Neural Computing and Applications, 33(10), 5011-5042, 2021.
  • J. S. Lavine, A. A. King, and O. N. Bjørnstad, "Natural immune boosting in pertussis dynamics and the potential for long-term vaccine failure”, Proceedings of the National Academy of Sciences, 108(17), 7259-7264, 2011.
There are 23 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

İsmail Koç 0000-0003-1311-5918

Publication Date January 31, 2023
Submission Date September 9, 2022
Published in Issue Year 2023 Volume: 16 Issue: 1

Cite

APA Koç, İ. (2023). Coronavirüs Sürü Bağışıklığı Algoritması ile Otsu Tabanlı Optimal Çok Düzeyli Görüntü Eşiği. Bilişim Teknolojileri Dergisi, 16(1), 1-11. https://doi.org/10.17671/gazibtd.1172909