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

Ahmet Nusret Toprak; Ömür Şahin; Rifat Kurban

doi:10.17780/ksujes.1414212

EN TR

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

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

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

Öz

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.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Nusret Toprak ^*
0000-0003-4841-9508
Türkiye

Ömür Şahin
0000-0003-1213-7445
Türkiye

Rifat Kurban
0000-0002-0277-2210
Türkiye

Yayımlanma Tarihi

3 Eylül 2024

Gönderilme Tarihi

3 Ocak 2024

Kabul Tarihi

28 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 3

DOI

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

IZ

https://izlik.org/JA68WM78HK

Kaynak Göster

RIS / Bibtex

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