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Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 14

Image Segmentation Using Spiking Neural Network Based Edge Detection and Seeded Region Growing

İğnecikli Sinir Ağı Tabanlı Kenar Algılama ve Tohumlu Alan Genişletme Kullanılarak Görüntü Bölütleme

Mahmut Kılıçaslan(Ankara Üniversitesi)0000-0003-1117-7736
Pages
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Publication Date
2026-07-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed method has been tested on various images, and the results demonstrate superior performance in terms of edge detection accuracy and region consistency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Digital image segmentation is a critical step in image analysis, pattern recognition, and computer vision applications.
📊
Key Findings & Data:In this study, a hybrid method is proposed to improve the accuracy and efficiency of the segmentation process by combining Spiking Neural Network-based edge detection with the Seeded Region Growing algorithm.
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Conclusion & Impact:The proposed method has been tested on various images, and the results demonstrate superior performance in terms of edge detection accuracy and region consistency.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Tohumlu Bölge Büyütme · Çivileme Sinir Ağı · İletilen Entegre ve Ateşleme

Abstract

Digital image segmentation is a critical step in image analysis, pattern recognition, and computer vision applications. In this study, a hybrid method is proposed to improve the accuracy and efficiency of the segmentation process by combining Spiking Neural Network-based edge detection with the Seeded Region Growing algorithm. Initially, the edges of the image are detected by leveraging the biologically inspired structure of Spiking Neural Networks, allowing for more precise localization of object boundaries. The resulting edge map is then used to guide the Seeded Region Growing algorithm. After edge detection, the remaining homogeneous regions are expanded using Seeded Region Growing, completing the segmentation process. The proposed method has been tested on various images, and the results demonstrate superior performance in terms of edge detection accuracy and region consistency.

Özet

Dijital görüntü segmentasyonu, görüntü analizi, desen tanıma ve bilgisayarlı görme uygulamalarında kritik bir adımdır. Bu çalışmada, Spiking Sinir Ağı tabanlı kenar algılama ile Tohumlu Bölge Büyütme algoritmasını birleştirerek segmentasyon sürecinin doğruluğunu ve verimliliğini artırmak için hibrit bir yöntem önerilmiştir. Başlangıçta, görüntünün kenarları Spiking Sinir Ağlarının biyolojik olarak ilham alan yapısından yararlanılarak tespit edilir ve bu da nesne sınırlarının daha hassas bir şekilde belirlenmesini sağlar. Elde edilen kenar haritası daha sonra Tohumlu Bölge Büyütme algoritmasına rehberlik etmek için kullanılır. Kenar tespitinden sonra, kalan homojen bölgeler Tohumlu Bölge Büyütme kullanılarak genişletilerek segmentasyon süreci tamamlanır. Önerilen yöntem çeşitli görüntüler üzerinde test edilmiş ve sonuçlar kenar algılama doğruluğu ve bölge tutarlılığı açısından üstün performans göstermiştir.

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Cite This Article

Kılıçaslan, M. (2026). Image Segmentation Using Spiking Neural Network Based Edge Detection and Seeded Region Growing. Balkan Journal of Electrical and Computer Engineering, Cilt: 14. https://doi.org/10.17694/bajece.1758646

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