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SÜRÜ ZEKASI TEMELLİ OPTİMİZASYON ALGORİTMALARI KULLANILARAK ÇOK SEVİYELİ EŞİKLEME İLE BEYİN MR GÖRÜNTÜLERİNİN BÖLÜTLENMESİ

Year 2024, , 726 - 754, 03.09.2024

Ahmet Nusret Toprak , Ömür Şahin , Rifat Kurban

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

Abstract

Bir görüntüyü bölüt adı verilen çeşitli piksel kümelerine ayırma işlemi olan görüntü bölütleme, görüntü işlemede önemli bir tekniktir. Görüntü bölütleme, görüntünün karmaşıklığını azaltmakta ve görüntüyü bölütlere ayırarak analiz edilmesini kolaylaştırmaktadır. Görüntü bölütlemenin en basit ancak etkin yollarından biri, piksellerin değerlerine göre birden çok bölgeye ayrıldığı çok düzeyli eşiklemedir. Bu çalışma, yaygın kullanılan sürü tabanlı optimizasyon algoritmalarının beyin MR görüntülerinde çok düzeyli eşikleme tabanlı görüntü bölütleme performansını araştırmayı ve karşılaştırmayı amaçlamaktadır. Yedi sürü zekâsı temelli optimizasyon algoritması: Parçacık Sürü Optimizasyonu (PSO), Yapay Arı Kolonisi (ABC), Gri Kurt Optimize Edici (GWO), Güve Alevi Optimizasyonu (MFO), Karınca Aslanı Optimizasyonu (ALO), Balina Optimizasyonu (WOA) ve Denizanası Arama Optimizasyon (JS) eşik seviyelerini belirlemek üzere beyin MR görüntülerine uygulanarak karşılaştırılmaktadır. Bahsi geçen algoritmalar ile yapılan deneylerde minimum çapraz entropi ve sınıflar arası varyans amaç fonksiyonları kullanılmıştır. Kapsamlı deneyler, JS, ABC ve PSO algoritmalarının daha iyi performans sergilediğini göstermektedir.

Keywords

görüntü bölütleme çok seviyeli eşikleme sürü zekası tabanlı optimizasyon minimum çapraz entropi sınıflar arası varyans

References

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  • Aslan, S., Demirci, S., Oktay, T., & Yesilbas, E. (2023). Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization. Biomimetics, 8(6), 486.
  • Aziz, M. A. El, Ewees, A. A., & Hassanien, A. E. (2017). Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 83. https://doi.org/10.1016/j.eswa.2017.04.023
  • Bakhshali, M. A., & Shamsi, M. (2014). Segmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO). Journal of Computational Science, 5(2). https://doi.org/10.1016/j.jocs.2013.07.001
  • Brajevic, I., & Tuba, M. (2014). Cuckoo search and firefly algorithm applied to multilevel image thresholding. Studies in Computational Intelligence, 516. https://doi.org/10.1007/978-3-319-02141-6_6
  • Chakrabarty, N. (2019). Brain MRI Images for Brain Tumor Detection. Retrieved from https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
  • Chou, J.-S., & Truong, D.-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535. https://doi.org/10.1016/j.amc.2020.125535
  • Cuevas, E., Zaldivar, D., & Pérez-Cisneros, M. (2010). A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Systems with Applications, 37(7). https://doi.org/10.1016/j.eswa.2010.01.013
  • Dhal, K. G., Das, A., Ray, S., Gálvez, J., & Das, S. (2020). Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation. In Archives of Computational Methods in Engineering (Vol. 27). Springer Netherlands. https://doi.org/10.1007/s11831-019-09334-y
  • Gao, H., Fu, Z., Pun, C. M., Hu, H., & Lan, R. (2018). A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm. Computers and Electrical Engineering, 70. https://doi.org/10.1016/j.compeleceng.2017.12.037
  • Ghamisi, P., Couceiro, M. S., Martins, F. M. L., & Benediktsson, J. A. (2014). Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 52(5). https://doi.org/10.1109/TGRS.2013.2260552
  • Gharehchopogh, F. S., & Ibrikci, T. (2024). An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation. Multimedia Tools and Applications, 83(6). https://doi.org/10.1007/s11042-023-16300-1
  • Guo, H., Li, M., Liu, H., Chen, X., Cheng, Z., Li, X., … He, Q. (2024). Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images. Computers in Biology and Medicine, 168(August 2023), 107769. https://doi.org/10.1016/j.compbiomed.2023.107769
  • Hammouche, K., Diaf, M., & Siarry, P. (2008). A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Computer Vision and Image Understanding, 109(2). https://doi.org/10.1016/j.cviu.2007.09.001
  • Hammouche, K., Diaf, M., & Siarry, P. (2010). A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Engineering Applications of Artificial Intelligence, 23(5), 676–688. https://doi.org/10.1016/j.engappai.2009.09.011
  • Jena, B., Naik, M. K., Panda, R., & Abraham, A. (2021). Maximum 3D Tsallis entropy based multilevel thresholding of brain MR image using attacking Manta Ray foraging optimization. Engineering Applications of Artificial Intelligence, 103(April), 104293. https://doi.org/10.1016/j.engappai.2021.104293
  • Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (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
  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization.
  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471. https://doi.org/10.1007/s10898-007-9149-x
  • 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). https://doi.org/10.17780/ksujes.1222041
  • Kaur, T., Saini, B. S., & Gupta, S. (2016). Optimized multi threshold brain tumor image segmentation using two dimensional minimum cross entropy based on co-occurrence matrix. In Studies in Computational Intelligence (Vol. 651). https://doi.org/10.1007/978-3-319-33793-7_20
  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948. IEEE. https://doi.org/10.1109/ICNN.1995.488968
  • Kotte, S., Pullakura, R. K., & Injeti, S. K. (2018). Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization. Measurement, 130, 340–361. https://doi.org/10.1016/j.measurement.2018.08.007
  • Kurban, R., Durmus, A., & Karakose, E. (2021). A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding. Engineering Applications of Artificial Intelligence, 105(July), 104410. https://doi.org/10.1016/j.engappai.2021.104410
  • Kurban, T., Civicioglu, P., Kurban, R., & Besdok, E. (2014). Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Applied Soft Computing Journal, 23. https://doi.org/10.1016/j.asoc.2014.05.037
  • Li, C. H., & Lee, C. K. (1993). Minimum cross entropy thresholding. Pattern Recognition, 26(4), 617–625. https://doi.org/10.1016/0031-3203(93)90115-D
  • Liu, Y., Mu, C., Kou, W., & Liu, J. (2015). Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Computing, 19(5). https://doi.org/10.1007/s00500-014-1345-2
  • Manikandan, S., Ramar, K., Willjuice Iruthayarajan, M., & Srinivasagan, K. G. (2014). Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm. Measurement: Journal of the International Measurement Confederation, 47(1). https://doi.org/10.1016/j.measurement.2013.09.031
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 50–60.
  • Mirjalili, S. (2015a). 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
  • Mirjalili, S. (2015b). The Ant Lion Optimizer. Advances in Engineering Software, 83, 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
  • Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Mittal, A., Soundararajan, R., & Bovik, A. C. (2013). Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3). https://doi.org/10.1109/LSP.2012.2227726
  • Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., & Perez-Cisneros, M. (2013). Multilevel thresholding segmentation based on harmony search optimization. Journal of Applied Mathematics, 2013. https://doi.org/10.1155/2013/575414
  • Oliva, D., Hinojosa, S., Cuevas, E., Pajares, G., Avalos, O., & Gálvez, J. (2017). Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Systems with Applications, 79, 164–180. https://doi.org/10.1016/j.eswa.2017.02.042
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
  • Portes de Albuquerque, M., Esquef, I. A., Gesualdi Mello, A. R., & Portes de Albuquerque, M. (2004). Image thresholding using Tsallis entropy. Pattern Recognition Letters, 25(9), 1059–1065. https://doi.org/10.1016/j.patrec.2004.03.003
  • Rahkar Farshi, T., & K. Ardabili, A. (2021). A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimedia Systems, 27(1), 125–142. https://doi.org/10.1007/s00530-020-00716-y
  • Rodríguez-Esparza, E., Zanella-Calzada, L. A., Oliva, D., Heidari, A. A., Zaldivar, D., Pérez-Cisneros, M., & Foong, L. K. (2020). An efficient Harris hawks-inspired image segmentation method. Expert Systems with Applications, 155. https://doi.org/10.1016/j.eswa.2020.113428
  • 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
  • Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–165. https://doi.org/10.1117/1.1631316
  • Sharma, A., Chaturvedi, R., & Bhargava, A. (2022). A novel opposition based improved firefly algorithm for multilevel image segmentation. Multimedia Tools and Applications, 81(11). https://doi.org/10.1007/s11042-022-12303-6
  • Tarkhaneh, O., & Shen, H. (2019). An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation. Expert Systems with Applications, 138. https://doi.org/10.1016/j.eswa.2019.07.037
  • Tuba, E., Alihodzic, A., & Tuba, M. (2017). Multilevel image thresholding using elephant herding optimization algorithm. 2017 14th International Conference on Engineering of Modern Electric Systems, EMES 2017. 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). https://doi.org/10.1109/TIP.2003.819861
  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
  • Ye, Z. W., Wang, M. W., Liu, W., & Chen, S. Bin. (2015). Fuzzy entropy based optimal thresholding using bat algorithm. Applied Soft Computing Journal, 31. https://doi.org/10.1016/j.asoc.2015.02.012

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

Year 2024, , 726 - 754, 03.09.2024

Ahmet Nusret Toprak , Ömür Şahin , Rifat Kurban

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

Abstract

Image segmentation, the process of dividing an image into various sets of pixels called segments, is an essential technique in image processing. Image segmentation reduces the complexity of the image and makes it easier to analyze by dividing the image into segments. One of the simplest yet powerful ways of image segmentation is multilevel thresholding, in which pixels are segmented into multiple regions according to their intensities. This study aims to explore and compare the performance of the well-known swarm-based optimization algorithms on the multilevel thresholding-based image segmentation task using brain MR images. Seven swarm-based optimization algorithms: Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Gray Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Ant Lion Optimization (ALO), Whale Optimization (WOA), and Jellyfish Search Optimizer (JS) algorithms are compared by applying to brain MR images to determine threshold levels. In the experiments carried out with mentioned algorithms, minimum cross-entropy, and between-class variance objective functions were employed. Extensive experiments show that JS, ABC, and PSO algorithms outperform others.

Keywords

image segmentation multilevel thresholding swarm-based optimization minimum cross-entropy between-class variance

References

  • Akay, B. (2013). A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Applied Soft Computing Journal, 13(6). https://doi.org/10.1016/j.asoc.2012.03.072
  • Aslan, S., Demirci, S., Oktay, T., & Yesilbas, E. (2023). Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization. Biomimetics, 8(6), 486.
  • Aziz, M. A. El, Ewees, A. A., & Hassanien, A. E. (2017). Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 83. https://doi.org/10.1016/j.eswa.2017.04.023
  • Bakhshali, M. A., & Shamsi, M. (2014). Segmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO). Journal of Computational Science, 5(2). https://doi.org/10.1016/j.jocs.2013.07.001
  • Brajevic, I., & Tuba, M. (2014). Cuckoo search and firefly algorithm applied to multilevel image thresholding. Studies in Computational Intelligence, 516. https://doi.org/10.1007/978-3-319-02141-6_6
  • Chakrabarty, N. (2019). Brain MRI Images for Brain Tumor Detection. Retrieved from https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
  • Chou, J.-S., & Truong, D.-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535. https://doi.org/10.1016/j.amc.2020.125535
  • Cuevas, E., Zaldivar, D., & Pérez-Cisneros, M. (2010). A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Systems with Applications, 37(7). https://doi.org/10.1016/j.eswa.2010.01.013
  • Dhal, K. G., Das, A., Ray, S., Gálvez, J., & Das, S. (2020). Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation. In Archives of Computational Methods in Engineering (Vol. 27). Springer Netherlands. https://doi.org/10.1007/s11831-019-09334-y
  • Gao, H., Fu, Z., Pun, C. M., Hu, H., & Lan, R. (2018). A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm. Computers and Electrical Engineering, 70. https://doi.org/10.1016/j.compeleceng.2017.12.037
  • Ghamisi, P., Couceiro, M. S., Martins, F. M. L., & Benediktsson, J. A. (2014). Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 52(5). https://doi.org/10.1109/TGRS.2013.2260552
  • Gharehchopogh, F. S., & Ibrikci, T. (2024). An improved African vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation. Multimedia Tools and Applications, 83(6). https://doi.org/10.1007/s11042-023-16300-1
  • Guo, H., Li, M., Liu, H., Chen, X., Cheng, Z., Li, X., … He, Q. (2024). Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images. Computers in Biology and Medicine, 168(August 2023), 107769. https://doi.org/10.1016/j.compbiomed.2023.107769
  • Hammouche, K., Diaf, M., & Siarry, P. (2008). A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Computer Vision and Image Understanding, 109(2). https://doi.org/10.1016/j.cviu.2007.09.001
  • Hammouche, K., Diaf, M., & Siarry, P. (2010). A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Engineering Applications of Artificial Intelligence, 23(5), 676–688. https://doi.org/10.1016/j.engappai.2009.09.011
  • Jena, B., Naik, M. K., Panda, R., & Abraham, A. (2021). Maximum 3D Tsallis entropy based multilevel thresholding of brain MR image using attacking Manta Ray foraging optimization. Engineering Applications of Artificial Intelligence, 103(April), 104293. https://doi.org/10.1016/j.engappai.2021.104293
  • Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (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
  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization.
  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471. https://doi.org/10.1007/s10898-007-9149-x
  • 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). https://doi.org/10.17780/ksujes.1222041
  • Kaur, T., Saini, B. S., & Gupta, S. (2016). Optimized multi threshold brain tumor image segmentation using two dimensional minimum cross entropy based on co-occurrence matrix. In Studies in Computational Intelligence (Vol. 651). https://doi.org/10.1007/978-3-319-33793-7_20
  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948. IEEE. https://doi.org/10.1109/ICNN.1995.488968
  • Kotte, S., Pullakura, R. K., & Injeti, S. K. (2018). Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization. Measurement, 130, 340–361. https://doi.org/10.1016/j.measurement.2018.08.007
  • Kurban, R., Durmus, A., & Karakose, E. (2021). A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding. Engineering Applications of Artificial Intelligence, 105(July), 104410. https://doi.org/10.1016/j.engappai.2021.104410
  • Kurban, T., Civicioglu, P., Kurban, R., & Besdok, E. (2014). Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Applied Soft Computing Journal, 23. https://doi.org/10.1016/j.asoc.2014.05.037
  • Li, C. H., & Lee, C. K. (1993). Minimum cross entropy thresholding. Pattern Recognition, 26(4), 617–625. https://doi.org/10.1016/0031-3203(93)90115-D
  • Liu, Y., Mu, C., Kou, W., & Liu, J. (2015). Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Computing, 19(5). https://doi.org/10.1007/s00500-014-1345-2
  • Manikandan, S., Ramar, K., Willjuice Iruthayarajan, M., & Srinivasagan, K. G. (2014). Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm. Measurement: Journal of the International Measurement Confederation, 47(1). https://doi.org/10.1016/j.measurement.2013.09.031
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 50–60.
  • Mirjalili, S. (2015a). 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
  • Mirjalili, S. (2015b). The Ant Lion Optimizer. Advances in Engineering Software, 83, 80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
  • Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Mittal, A., Soundararajan, R., & Bovik, A. C. (2013). Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3). https://doi.org/10.1109/LSP.2012.2227726
  • Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., & Perez-Cisneros, M. (2013). Multilevel thresholding segmentation based on harmony search optimization. Journal of Applied Mathematics, 2013. https://doi.org/10.1155/2013/575414
  • Oliva, D., Hinojosa, S., Cuevas, E., Pajares, G., Avalos, O., & Gálvez, J. (2017). Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Systems with Applications, 79, 164–180. https://doi.org/10.1016/j.eswa.2017.02.042
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
  • Portes de Albuquerque, M., Esquef, I. A., Gesualdi Mello, A. R., & Portes de Albuquerque, M. (2004). Image thresholding using Tsallis entropy. Pattern Recognition Letters, 25(9), 1059–1065. https://doi.org/10.1016/j.patrec.2004.03.003
  • Rahkar Farshi, T., & K. Ardabili, A. (2021). A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimedia Systems, 27(1), 125–142. https://doi.org/10.1007/s00530-020-00716-y
  • Rodríguez-Esparza, E., Zanella-Calzada, L. A., Oliva, D., Heidari, A. A., Zaldivar, D., Pérez-Cisneros, M., & Foong, L. K. (2020). An efficient Harris hawks-inspired image segmentation method. Expert Systems with Applications, 155. https://doi.org/10.1016/j.eswa.2020.113428
  • 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
  • Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–165. https://doi.org/10.1117/1.1631316
  • Sharma, A., Chaturvedi, R., & Bhargava, A. (2022). A novel opposition based improved firefly algorithm for multilevel image segmentation. Multimedia Tools and Applications, 81(11). https://doi.org/10.1007/s11042-022-12303-6
  • Tarkhaneh, O., & Shen, H. (2019). An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation. Expert Systems with Applications, 138. https://doi.org/10.1016/j.eswa.2019.07.037
  • Tuba, E., Alihodzic, A., & Tuba, M. (2017). Multilevel image thresholding using elephant herding optimization algorithm. 2017 14th International Conference on Engineering of Modern Electric Systems, EMES 2017. 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). https://doi.org/10.1109/TIP.2003.819861
  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
  • Ye, Z. W., Wang, M. W., Liu, W., & Chen, S. Bin. (2015). Fuzzy entropy based optimal thresholding using bat algorithm. Applied Soft Computing Journal, 31. https://doi.org/10.1016/j.asoc.2015.02.012
There are 48 citations in total.

Details

Primary Language English
Subjects Image Processing
Journal Section Computer Engineering
Authors

Ahmet Nusret Toprak 0000-0003-4841-9508

Ömür Şahin 0000-0003-1213-7445

Rifat Kurban 0000-0002-0277-2210

Publication Date September 3, 2024
Submission Date January 3, 2024
Acceptance Date March 28, 2024
Published in Issue Year 2024

Cite

APA Toprak, A. N., Şahin, Ö., & Kurban, R. (2024). MULTILEVEL THRESHOLDING FOR BRAIN MR IMAGE SEGMENTATION USING SWARM-BASED OPTIMIZATION ALGORITHMS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 726-754. https://doi.org/10.17780/ksujes.1414212