Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Year 2020, Ejosat Special Issue 2020 (ICCEES), 404 - 411, 05.10.2020

Bashir Sheikh Abdullahi Jama , Dr. Nurdan Baykan

https://doi.org/10.31590/ejosat.812052

Abstract

Görüntü bölütleme, çeşitli alanlar için geçerli olan görüntü işlemenin önemli bir adımıdır. Bu alanlar arasında makine görmesi, nesne algılama, astronomi, biyometrik tanıma sistemleri (yüz, parmak izi, plaka ve göz), tıbbi görüntüleme, video izleme ve diğer birçok görüntü tabanlı teknoloji bulunmaktadır. Etkili görüntü bölütleme, otomatik görüntü işlemede en önemli işlemlerden ve kritik rollerden biridir. Özellikle mühendislik çalışmalarında, problemlerde en uygun çözümleri bulmak önemli araştırma konularından biridir. Arama alanlarında en uygun çözümleri bulmak için Parçacık Sürü Optimizasyonu (PSO), Karınca Algoritması (KA), Yapay Arı Kolonisi (ABC) ve Yarasa Algoritması (YA) gibi biyo-esinlenmiş algoritmalar kullanılır ve Karınca Aslan Optimizasyonu (KAO) bu algoritmalardan biridir. Son yıllarda, görüntülerin bölütleme parametrelerini optimize etmek için biyo-esinlenmiş algoritmalar kullanılmaktadır. Bu araştırma, biyo-esinlenmiş KAO kullanarak, geliştirilmiş bir bölge büyütme (BB) görüntü bölütleme yaklaşımı önermektedir. Bölge büyütme doğru tohum seçimi, tohum sayısı ve bölge yetiştirme stratejisi olmak üzere üç ana sorunu vardır. Bu nedenle RG’deki doğru tohum probleminin seçiminde KAO kullanılmıştır. Bu çalışmada öncelikle görüntülerin kalitesini artırmak için, giriş görüntülerine ortanca filtresi uygulanmıştır. Daha sonra KAO’dan elde edilen optimum tohum noktaları kullanılarak bölge büyütme ile bölütleme işlemi gerçekleştirilmiştir. Optimal tohumları elde etmek için, bölütleme sırasında BB'nin sınırlamalarını çözmek için KAO kullanılmıştır. Önerilen yaklaşımın başarısı, BSDS300 (Berkeley) veri setinden alınan bazı görüntüler kullanılarak test edilmiştir. Deneysel sonuçlar, önerilen yöntemin neredeyse tüm görüntüleri bölütlere ayırdığını göstermektedir.

Keywords

Bölge büyütme, tohum seçimi, görüntü bölütleme, önişleme, Karınca Aslan Optimizasyonu

Modified Region Growing Method For Image Segmentation Using Ant Lion Optimization Algorithm

Abstract

Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.

Keywords

Region growing, Seed point selection, Image Segmentation, pre-processing, Ant Lion Optimization

References

• Brice, C.R. and C.L. Fennema, Scene analysis using regions. Artificial intelligence, 1970. 1(3-4): p. 205-226.
• Bhargavi, K. and S. Jyothi, A survey on threshold based segmentation technique in image processing. International Journal of Innovative Research and Development, 2014. 3(12): p. 234-239.
• Chhabra, A., A. Gupta, and A. Victor, Comparison of Image Segmentation Algorithms. International Journal of Emerging Trends & Technology in Computer Science, 2013. 2(3): p. 14-17.
• Kumar, V., et al. A study and comparison of different image segmentation algorithms. in 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Fall). 2016. IEEE.
• Jaglan, P., R. Dass, and M. Duhan. A comparative analysis of various image segmentation techniques. in Proceedings of 2nd International Conference on Communication, Computing and Networking. 2019. Springer.
• Merzougui, M. and A. El Allaoui, Region growing segmentation optimized by evolutionary approach and Maximum Entropy. Procedia Computer Science, 2019. 151: p. 1046-1051.
• Jeevakala, S. and R. Rangasami, A novel segmentation of cochlear nerve using region growing algorithm. Biomedical Signal Processing and Control, 2018. 39: p. 117-129.
• Reddy, A.S. and P.C. Reddy. Novel Algorithm based on Region Growing Method for Better Image Segmentation. in 2018 3rd International Conference on Communication and Electronics Systems (ICCES). 2018. IEEE.
• Chondro, P., et al., Low order adaptive region growing for lung segmentation on plain chest radiographs. Neurocomputing, 2018. 275: p. 1002-1011.
• Charifi, R., et al. Comparative Study of Color Image Segmentation by the Seeded Region Growing Algorithm. in 2018 IEEE 5th International Congress on Information Science and Technology (CiSt). 2018. IEEE.
• Punitha, S., A. Amuthan, and K.S. Joseph, Benign and malignant breast cancer segmentation using optimized region growing technique. Future Computing and Informatics Journal, 2018. 3(2): p. 348-358.
• Bruntha, P.M. and M. Kanimozhi. Application Of Selective Region Growing Algorithm In Lung Nodule Segmentation. in 2018 4th International Conference on Devices, Circuits and Systems (ICDCS). 2018. IEEE.
• Baghi, A. and A. Karami. SAR image segmentation using region growing and spectral cluster. in 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). 2017. IEEE.
• Duman, E. and O.A. Erdem. A new image denoising method based on region growing segmentation. in 2017 25th Signal Processing and Communications Applications Conference (SIU). 2017. IEEE.
• Malarvel, M., et al. Region growing based segmentation with automatic seed selection using threshold techniques on X-radiography images. in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). 2016. IEEE.
• Happ, P.N., et al. Towards distributed region growing image segmentation based on MapReduce. in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015. IEEE.
• Zhongming, L. and W. Jun. The image segmentation algorithm of region growing and wavelet transform modulus maximum. in 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). 2015. IEEE.
• Elmorsy, S.A., et al. K3. A region growing liver segmentation method with advanced morphological enhancement. in 2015 32nd National Radio Science Conference (NRSC). 2015. IEEE.
• Singh, A.K. and B. Gupta, A novel approach for breast cancer detection and segmentation in a mammogram. Procedia Computer Science, 2015. 54: p. 676-682.
• Li, X., et al. A new region growing-based segmentation method for high resolution remote sensing imagery. in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015. IEEE.
• Wu, J. and H. Guo. A method for sonar image segmentation based on combination of MRF and region growing. in 2015 Fifth International Conference on Communication Systems and Network Technologies. 2015. IEEE.
• Zhang, X., X. Li, and Y. Feng, A medical image segmentation algorithm based on bi-directional region growing. Optik, 2015. 126(20): p. 2398-2404.
• Lu, X., et al., The study and application of the improved region growing algorithm for liver segmentation. Optik-International Journal for Light and Electron Optics, 2014. 125(9): p. 2142-2147.
• Mirghasemi, S., R. Rayudu, and M. Zhang. A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization. in 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013). 2013. IEEE.
• Neri, F. and C. Cotta, Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2012. 2: p. 1-14.
• Talbi, E.-G., Metaheuristics: from design to implementation. Vol. 74. 2009: John Wiley & Sons.
• Mitchell, M., Genetic algorithms: An overview. Complexity, 1995. 1(1): p. 31-39.
• Aarts, E., J. Korst, and W. Michiels, Simulated annealing, in Search methodologies. 2005, Springer. p. 187-210.
• Qin, A.K., V.L. Huang, and P.N. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE transactions on Evolutionary Computation, 2008. 13(2): p. 398-417.
• Dorigo, M. and T. Stützle, The ant colony optimization metaheuristic: Algorithms, applications, and advances, in Handbook of metaheuristics. 2003, Springer. p. 250-285.
• Dorigo, M. and M. Birattari, Ant colony optimization. 2010: Springer.
• Dorigo, M. and C. Blum, Ant colony optimization theory: A survey. Theoretical computer science, 2005. 344(2-3): p. 243-278.
• Eberhart, R. and J. Kennedy. A new optimizer using particle swarm theory. in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. 1995. Ieee.
• Mahdavi, M., M. Fesanghary, and E. Damangir, An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation, 2007. 188(2): p. 1567-1579.
• Lee, K.S. and Z.W. Geem, A new structural optimization method based on the harmony search algorithm. Computers & structures, 2004. 82(9-10): p. 781-798.
• Yang, X.-S., A new metaheuristic bat-inspired algorithm, in Nature inspired cooperative strategies for optimization (NICSO 2010). 2010, Springer. p. 65-74.
• Back, T., Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. 1996: Oxford university press.
• Mirjalili, S., S.M. Mirjalili, and A. Lewis, Grey wolf optimizer. Advances in engineering software, 2014. 69: p. 46-61.
• Karaboga, D. and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 2007. 39(3): p. 459-471.
• Rajinikanth, V. and M. Couceiro, RGB histogram based color image segmentation using firefly algorithm. Procedia Computer Science, 2015. 46: p. 1449-1457.
• Gandomi, A.H., X.-S. Yang, and A.H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, 2013. 29(1): p. 17-35.
• Rashedi, E., H. Nezamabadi-Pour, and S. Saryazdi, GSA: a gravitational search algorithm. Information sciences, 2009. 179(13): p. 2232-2248.
• Rajabioun, R., Cuckoo optimization algorithm. Applied soft computing, 2011. 11(8): p. 5508-5518.
• Kaveh, A. and S. Talatahari, A novel heuristic optimization method: charged system search. Acta Mechanica, 2010. 213(3-4): p. 267-289.
• Kaveh, A. and M. Khayatazad, A new meta-heuristic method: ray optimization. Computers & structures, 2012. 112: p. 283-294.
• Kaveh, A. and V. Mahdavi, Colliding bodies optimization: a novel meta-heuristic method. Computers & Structures, 2014. 139: p. 18-27.
• Neshat, M., G. Sepidnam, and M. Sargolzaei, Swallow swarm optimization algorithm: a new method to optimization. Neural Computing and Applications, 2013. 23(2): p. 429-454.
• Kaveh, A. and A. Zolghadr, Democratic PSO for truss layout and size optimization with frequency constraints. Computers & Structures, 2014. 130: p. 10-21.
• Kaveh, A. and N. Farhoudi, A new optimization method: Dolphin echolocation. Advances in Engineering Software, 2013. 59: p. 53-70.
• Liu, B., et al., Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 2005. 25(5): p. 1261-1271.
• Mirjalili, S. and A. Lewis, The whale optimization algorithm. Advances in engineering software, 2016. 95: p. 51-67.
• Mirjalili, S., The ant lion optimizer. Advances in Engineering Software, 2015. 83: p. 80-98.
• Sharma, A. and S. Sehgal. Image segmentation using firefly algorithm. in 2016 International Conference on Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect your Worlds. 2016. IEEE.
• Zanaty, E.A. and A.S. Ghiduk, A novel approach based on genetic algorithms and region growing for magnetic resonance image (MRI) segmentation. Comput. Sci. Inf. Syst., 2013. 10(3): p. 1319-1342.
• Sağ, T. and M. Çunkaş, Color image segmentation based on multiobjective artificial bee colony optimization. Applied soft computing, 2015. 34: p. 389-401.
• Mostafa, A., et al. Antlion optimization based segmentation for MRI liver images. in International Conference on Genetic and Evolutionary Computing. 2016. Springer.
• Li, L., et al., Modified discrete grey wolf optimizer algorithm for multilevel image thresholding. Computational intelligence and neuroscience, 2017. 2017.
• Koc, I., O.K. Baykan, and I. Babaoglu, Gri kurt optimizasyon algoritmasına dayanan çok seviyeli imge eşik seçimi. Politeknik Dergisi. 21(4): p. 841-847.
• Mao, X., et al. Color image segmentation method based on region growing and ant colony clustering. in 2009 WRI Global Congress on Intelligent Systems. 2009. IEEE.