THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM

Murat Karakoyun

doi:10.17780/ksujes.1222041

TR EN

EŞİKLEME METOTLARININ SEGMENTASYON ÜZERİNDEKİ ETKİLERİNİN GÜVE ALEV OPTİMİZASYONU ALGORİTMASI KULLANILARAK KARŞILAŞTIRILMASI

Öz

Segmentasyon görüntü işleme uygulamalarında başarıyı doğrudan etkileyen önemli bir ön işlem adımıdır. Segmentasyon süreci için kullanılan birçok yöntem ve yaklaşım mevcuttur. Eşikleme bu yöntemler içerisinde sıklıkla kullanılan bir yaklaşımdır. Eşikleme için önerilen birçok yaklaşım bulunmaktadır. Bu çalışmada moth flame algoritması kullanılarak altı farklı eşikleme yaklaşımı uygunluk fonksiyonu olarak kullanılmış ve bu yaklaşımlardan elde edilen sonuçlar karşılaştırılmıştır. Deneysel çalışmalarda 10 farklı görüntünün yedi farklı eşik seviyesi üzerinde çalışılmıştır. Üç farklı metrik ile yapılan kıyaslamalarda Otsu metodunun genel olarak daha başarılı olduğu görülmüştür. Ayrıca minimum cross entropy ve Renyi entropilerinin de alternatif olarak kullanılabileceği gözlemlenmiştir.

Anahtar Kelimeler

THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM

Abstract

Segmentation is an important preprocessing step that directly affects the success in image processing applications. There are many methods and approaches used for the segmentation process. Thresholding is a frequently used approach among these methods. There are several suggested approaches to thresholding. In this study, six different thresholding approaches were used as the fitness functions using the moth flame algorithm and the results obtained from these approaches were compared. In experimental studies, seven different threshold levels of 10 different images were studied. In comparisons made with three different metrics, it was seen that the Otsu method was generally more successful. It has also been observed that the minimum cross entropy and Renyi entropies can be used as alternatives.

Keywords

Kaynakça

Abdel-Basset, M., Mohamed, R., AbdelAziz, N. M., & Abouhawwash, M. (2022). HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation. Expert Systems with Applications, 190, 116145. https://doi.org/10.1016/j.eswa.2021.116145
Bhandari, A. K., Kumar, A., & Singh, G. K. (2015a). Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications, 42(3), 1573-1601. https://doi.org/10.1016/j.eswa.2014.09.049
Bhandari, A. K., Kumar, A., & Singh, G. K. (2015b). Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Systems with Applications, 42(22), 8707-8730. https://doi.org/10.1016/j.eswa.2015.07.025
Brooks, A. C., Zhao, X., & Pappas, T. N. (2008). Structural similarity quality metrics in a coding context: exploring the space of realistic distortions. IEEE Transactions on image processing, 17(8), 1261-1273. https://doi.org/10.1109/TIP.2008.926161 Cai, Y., Mi, S., Yan, J., Peng, H., Luo, X., Yang, Q., & Wang, J. (2022). An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Information Sciences, 587, 473-484. https://doi.org/10.1016/j.ins.2021.12.058
Chen, Y., Wang, M., Heidari, A. A., Shi, B., Hu, Z., Zhang, Q., Chen, H., Mafarja, M., & Turabieh, H. (2022). Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm. Expert Systems with Applications, 194, 116511. https://doi.org/10.1016/j.eswa.2022.116511 https://doi.org/10.1016/j.eswa.2022.116511
De Albuquerque, M. P., Esquef, I. A., Mello, A. R. G., & De Albuquerque, M. P. (2004). Image thresholding using Tsallis entropy. Pattern Recognition Letters, 25(9), 1059-1065. https://doi.org/10.1016/j.patrec.2004.03.003
Günay, M., & Taze, M. (2022). Mikroskobik Görüntülerde Multipl Miyelom Plazma Hücrelerinin Tespiti. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(2), 145-154. https://doi.org/10.17780/ksujes.1120829
Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. Paper presented at the 2010 20th international conference on pattern recognition.https://doi.org/ 10.1109/ICPR.2010.579

Houssein, E. H., Helmy, B. E., Oliva, D., Jangir, P., Premkumar, M., Elngar, A. A., & Shaban, H. (2022). An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer. Biomedical Signal Processing and Control, 73, 103401. https://doi.org/10.1016/j.bspc.2021.103401
Huang, C., Li, X., & Wen, Y. (2021). AN OTSU image segmentation based on fruitfly optimization algorithm. Alexandria Engineering Journal, 60(1), 183-188. https://doi.org/10.1016/j.aej.2020.06.054
Ishak, A. B. (2017). A two-dimensional multilevel thresholding method for image segmentation. Applied Soft Computing, 52, 306-322. https://doi.org/10.1016/j.asoc.2016.10.034
Kalyani, R., Sathya, P., & Sakthivel, V. (2020). Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy. Engineering Science and Technology, an International Journal, 23(6), 1327-1341. https://doi.org/10.1016/j.jestch.2020.07.007
Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285. https://doi.org/10.1016/0734-189X(85)90125-2
Karakoyun, M., Baykan, N. A., & Hacibeyoglu, M. (2017a). Multi-level thresholding for image segmentation with swarm optimization algorithms. International Research Journal of Electronics & Computer Engineering, 3(3), 1. https://doi.org/10.24178/irjece.2017.3.3.01
Karakoyun, M., Gülcü, Ş., & Kodaz, H. (2021). D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding. Engineering Science and Technology, an International Journal, 24(6), 1455-1466. https://doi.org/10.1016/j.jestch.2021.03.011
Karakoyun, M., & Özkış, A. (2021). Transfer Fonksiyonları Kullanarak İkili Güve-Alev Optimizasyonu Algoritmalarının Geliştirilmesi ve Performanslarının Karşılaştırılması. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 3(2), 1-10.
Karakoyun, M., Saglam, A., Baykan, N. A., & Altun, A. A. (2017b). Non-locally color image segmentation for remote sensing images in different color spaces by using data-clustering methods. image, 10, 11.
Koc, I., Baykan, O. K., & Babaoglu, I. (2018). Multilevel image thresholding selection based on grey wolf optimizer. JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 21(4), 841-847. https://doi.org/10.2339/politeknik.389613
Li, Y., Cao, G., Wang, T., Cui, Q., & Wang, B. (2020). A novel local region-based active contour model for image segmentation using Bayes theorem. Information Sciences, 506, 443-456. https://doi.org/10.1016/j.ins.2019.08.021
Li, Y., Zhu, X., & Liu, J. (2020). An improved moth-flame optimization algorithm for engineering problems. Symmetry, 12(8), 1234. https://doi.org/10.3390/sym12081234
Ma, G., & Yue, X. (2022). An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Engineering Applications of Artificial Intelligence, 113, 104960. https://doi.org/10.1016/j.engappai.2022.104960
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
Naidu, M., Kumar, P. R., & Chiranjeevi, K. (2018). Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Engineering Journal, 57(3), 1643-1655. https://doi.org/10.1016/j.aej.2017.05.024
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
Pal, N. R. (1996). On minimum cross-entropy thresholding. Pattern recognition, 29(4), 575-580. https://doi.org/10.1016/0031-3203(95)00111-5
Priyadharsini, R., & Sharmila, T. S. (2019). Object detection in underwater acoustic images using edge based segmentation method. Procedia Computer Science, 165, 759-765. https://doi.org/10.1016/j.procs.2020.01.015
Raj, A., Gautam, G., Abdullah, S. N. H. S., Zaini, A. S., & Mukhopadhyay, S. (2019). Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy. Image and Vision Computing, 91, 103792. https://doi.org/10.1016/j.imavis.2019.07.004
Ryalat, M. H., Dorgham, O., Tedmori, S., Al-Rahamneh, Z., Al-Najdawi, N., & Mirjalili, S. (2022). Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. Neural Computing and Applications, 1-19. https://doi.org/10.1007/s00521-022-08078-4
Sahoo, P., Wilkins, C., & Yeager, J. (1997). Threshold selection using Renyi's entropy. Pattern recognition, 30(1), 71-84. https://doi.org/10.1016/S0031-3203(96)00065-9
Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications, 7(3), 8-18.https://doi.org/10.4236/jcc.2019.73002
Satapathy, S. C., Sri Madhava Raja, N., Rajinikanth, V., Ashour, A. S., & Dey, N. (2018). Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Computing and Applications, 29(12), 1285-1307. https://doi.org/10.1007/s00521-016-2645-5
Selçuk, T., Bilal, N., Sarıca, S., Akben, B., & Alkan, A. (2017). Ses Tellerinde Oluşan Nodüllere Ait Şekilsel Özelliklerin Görüntü İşleme Teknikleriyle Otomatik Olarak Belirlenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 20(4), 54-59. https://doi.org/10.17780/ksujes.349448
Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., & Khasawneh, A. M. (2020). Moth–flame optimization algorithm: variants and applications. Neural Computing and Applications, 32(14), 9859-9884. https://doi.org/10.1007/s00521-019-04570-6
Tuba, E., Alihodzic, A., & Tuba, M. (2017). Multilevel image thresholding using elephant herding optimization algorithm. Paper presented at the 2017 14th international conference on engineering of modern electric systems (EMES). https://doi.org/10.1109/EMES.2017.7980424
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on image processing, 13(4), 600-612. https://doi.org/10.1109/TIP.2003.819861
Xing, Z., & He, Y. (2021). Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm. Applied Soft Computing, 113, 107905. https://doi.org/10.1016/j.asoc.2021.107905
Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on image processing, 20(8), 2378-2386.https://doi.org/10.1109/TIP.2011.2109730
Zhao, S., Wang, P., Heidari, A. A., Chen, H., Turabieh, H., Mafarja, M., & Li, C. (2021). Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease. Computers in Biology and Medicine, 134, 104427. https://doi.org/10.1016/j.compbiomed.2021.104427

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Murat Karakoyun ^*
0000-0002-0677-9313
Türkiye

Yayımlanma Tarihi

3 Haziran 2023

Gönderilme Tarihi

20 Aralık 2022

Kabul Tarihi

26 Şubat 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 2

DOI

https://doi.org/10.17780/ksujes.1222041

IZ

https://izlik.org/JA85LC47FB

Kaynak Göster

RIS / Bibtex

APA

Karakoyun, M. (2023). THE COMPARISON OF THE EFFECTS OF THRESHOLDING METHODS ON SEGMENTATION USING THE MOTH FLAME OPTIMIZATION ALGORITHM. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(2), 517-531. https://doi.org/10.17780/ksujes.1222041

Cited By

Elektron mikroskobundan elde edilen görüntülerin parçacık sayımında ön işleme tekniklerinin etkisi

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.54365/adyumbd.1348886

MULTILEVEL THRESHOLDING FOR BRAIN MR IMAGE SEGMENTATION USING SWARM-BASED OPTIMIZATION ALGORITHMS

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.17780/ksujes.1414212