KAN HÜCRELERİNİN OPTİMUM ODAKLI GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI YAKLAŞIMIN GELİŞTİRİLMESİ

Fatma Tuana Doğu; Zeinab Danaei; Hülya Doğan; Ramazan Özgür Doğan; Feride Sena Sezen

doi:10.17780/ksujes.1506248

EN TR

DEVELOPMENT OF A DEEP LEARNING-BASED APPROACH FOR OPTIMAL FOCUSED IMAGING OF BLOOD CELLS

Abstract

Due to the depth of focus in microscopic systems, samples containing blood cells cannot be imaged completely in focus. This may cause performance loss of artificial intelligence and image processing algorithms. To solve this, extended depth of field approaches are used and an optimally focused image of the sample is obtained. Although many extended depth of field approaches were developed in the literature, there are still various shortcomings in this field, such as high running time and different performance depending on the sample used and the type of microscope. To eliminate these shortcomings in the literature, a new dataset is created for optimally focused imaging of blood cells in microscopic systems and a new deep learning-based approach to depth of field is proposed. Four different criteria are used to evaluate the performance of the study: Perception-based Image Quality Evaluator, Blind Image Spatial Quality Evaluator, Blurring and Naturalness Image Quality. In the developed study, 13 different extended depth of field approaches are tested. In this study, the results of the performance evaluation criteria prove that the deep learning-based extended depth of field approach proposed for optimally focused imaging of blood cells is more performant than other approaches.

Keywords

KAN HÜCRELERİNİN OPTİMUM ODAKLI GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI YAKLAŞIMIN GELİŞTİRİLMESİ

Öz

Mikroskobik sistemlerde var olan odaklama derinliği sebebiyle kan hücreleri bulunan numuneler tamamıyla odaklı görüntülenememektedir. Bu durum yapay zeka ve görüntü işleme algoritmalarının performans kaybına sebep olabilmektedir. Bunu çözmek için odaklama derinliğinin artırılması yaklaşımları kullanılmakta ve numunenin optimum odaklı görüntüsü elde edilmektedir. Literatürde birçok odaklama derinliğinin artırılması yaklaşımı bulunmasına rağmen bu alanda hala yüksek çalışma süresi, kullanılan numuneye ve mikroskop çeşidine göre farklı performans gösterme gibi çeşitli eksiklikler mevcuttur. Bu çalışmada, literatürdeki bu eksiklikleri gidermek amacıyla mikroskobik sistemlerde kan hücrelerinin optimum odaklı görüntülenmesi için hem yeni veri seti oluşturulmakta hem de derin öğrenme tabanlı yeni bir odaklama derinliği artırılması yaklaşımı önerilmektedir. Çalışmanın performansını değerlendirmek için Algı Tabanlı Görüntü Kalitesi, Referanssız Görüntü Uzamsal Kalite, Bulanıklık ve Doğallık Görüntü Kalitesi olmak üzere dört farklı kriter kullanılmaktadır. Geliştirilen çalışmada 13 farklı odaklama derinliğinin artırılması yaklaşımı test edilmektedir. Bu çalışmada performans değerlendirme kriterleri sonuçları ile kan hücrelerinin optimum odaklı görüntülenmesi için önerilen derin öğrenme tabanlı odaklama derinliğinin artırılması yaklaşımının diğer yaklaşımlara göre daha performanslı olduğu ispatlanmaktadır.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Görüntü İşleme , Derin Öğrenme , Yapay Zeka (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Fatma Tuana Doğu ^*
0009-0008-0916-9394
Türkiye

Zeinab Danaei
0000-0002-5881-1960
Türkiye

Hülya Doğan
0000-0003-3695-8539
Türkiye

Ramazan Özgür Doğan
0000-0001-6415-5755
Türkiye

Feride Sena Sezen
0000-0002-7379-2518
Türkiye

Yayımlanma Tarihi

3 Aralık 2024

Gönderilme Tarihi

27 Haziran 2024

Kabul Tarihi

20 Kasım 2024

Yayımlandığı Sayı

Yıl 1970 Cilt: 27 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1506248

IZ

https://izlik.org/JA94HF99MA

Kaynak Göster

RIS / Bibtex

APA

Doğu, F. T., Danaei, Z., Doğan, H., Doğan, R. Ö., & Sezen, F. S. (2024). KAN HÜCRELERİNİN OPTİMUM ODAKLI GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI YAKLAŞIMIN GELİŞTİRİLMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1465-1476. https://doi.org/10.17780/ksujes.1506248