GÖRÜNTÜ FİLTRELEME İLE DENETİMSİZ RETİNA DAMAR BÖLÜTLENMESİ İÇİN PARAMETRE ENİYİLEŞTİRİLMESİ

Year 2022, , 844 - 855, 30.09.2022

Cem Yakut Sezer Ulukaya

https://doi.org/10.21923/jesd.1033339

Abstract

Göz hastalıklarının tespiti ve değerlendirilmesi için retina görüntüleri fundus adı verilen özelleştirilmiş bir kamera sistemi ile sayısal ortamda elde edilmektedir. Çeşitli gürültüler ve keskin olmayan zıtlık dolayısıyla gözdeki damarların uzmanlar tarafından tespiti zorlaşmakta ve bu durum uzmanların teşhis koymasını zorlaştırabilmektedir. Bu çalışmada, fundus görüntülerinden retina damar örgüsü bölütlenme başarısını arttırmak amacıyla denetimsiz görüntü işleme tabanlı matematiksel morfoloji ve Coye filtreleme ve bağlantılı bileşen analizi yaklaşımları kullanılmıştır. Ek olarak, retina görüntüleri gürültü giderme ve zıtlık arttırmak için ön işlemden geçirilmiştir. Denetimsiz görüntü işleme tabanlı yaklaşımların başarısını arttırmak üzere parametre optimizasyonu yapılmıştır. Görüntü işlemede sıklıkla kullanılan kontrast sınırlı adaptif histogram eşitleme (KSAHE) yönteminde renkli retina görüntüleri için en uygun kontrast üst sınır değeri araştırılmıştır. Önerilen yaklaşım, araştırmacıların erişimine açık DRIVE ve STARE veri kümelerinde test edilmiştir. Önceki denetimsiz öğrenme çalışmalarına kıyasla bazı metriklerde başabaş ve bazı metriklerde daha başarılı sonuçlara ulaşılmıştır.

Keywords

Retina Görüntüsü, Damar Örüntü Bölütlemesi, Denetimsiz Görüntü İşleme

PARAMETER OPTIMIZATION FOR UNSUPERVISED RETINAL VESSEL SEGMENTATION WITH IMAGE FILTERING

Abstract

For the detection and evaluation of eye disorders, retinal pictures are obtained in a digital environment with a customized camera system called the fundus. Due to various noises and unsharp contrast, it is difficult to detect the vessels in the eye by specialists, and this can make it difficult for specialists to diagnose. In this study, unsupervised image processing-based mathematical morphology and Coye filtering, and connected component analysis approaches were used to increase the success of retinal vascular segmentation from fundus images. In addition, retinal images are preprocessed for noise reduction and increased contrast. Parameter optimization was performed to increase the success of unsupervised image processing-based approaches. In the contrast limited adaptive histogram equalization (CLAHE) method, which is frequently used in image processing, the most appropriate upper limit value for contrast on color retinal images was investigated. The presented approach tested on the DRIVE and STARE datasets available to researchers. Compared to previous unsupervised learning studies, some metrics were at par with the literature and some metrics were more successful.

Keywords

Retinal Image, Vascular Pattern Segmentation, Unsupervised Image Processing

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