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

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

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