Meta-Sezgisel Algoritmalara Dayalı Retinal Damar Bölütleme

Diyabet hastalığına bağlı olarak retina tabakasına kan taşıyan kılcal damarlarda fonksiyon kayıpları oluşmakta ve Diyabetik Retinopati (Diabetic Retinopathy, DR) hastalığı ortaya çıkmaktadır. İlk aşamalarında gözde belirli oranlarda görme kayıplarına yol açan DR hastalığı doğru bir şekilde teşhis ve tedavi edilmez ise görme fonksiyonunun tamamen yok olmasına sebep olabilmektedir. DR hastalığının yüksek doğrulukta teşhis ve tedavi edilebilmesi için retinal damar yapısının bölütleme işlemi ile retina görüntüsünden ayrıştırılması ve analiz edilmesi gerekmektedir. Bu çalışmada, en güncel meta-sezgisel algoritmalardan olan Vahşi At Optimizasyon (Wild Horse Optimization, WHO) ve Kel Kartal Araştırma (Bald Eagle Search, BES) algoritmaları retinal damar bölütlemeye yönelik olarak kümeleme tabanlı geliştirilmiş ve performansları yaygın olarak kullanılan Gri Kurt Optimizasyon (Grey Wolf Optimization, GWO) algoritması ile mukayese edilmiştir.

Retinal Vessel Segmentation Based On Meta-Heuristic Algorithms

The functional losses due to the diabetes disease occurring in the vessels that carry blood to the retina layer causes the Diabetic Retinopathy (DR) disease. The DR which causes vision loss at certain rate in its initial stages, can lead to complete destruction of visual function if it is not correctly diagnosed and treated. In order to diagnose and treat DR with high accuracy, retinal vessel structure should be separated from the retinal image by segmentation and then to be analyzed in detail. In this work, Wild Horse Optimization (WHO) and Bald Eagle Search (Bald Eagle Search, BES) algorithms which are among the most recently proposed meta-heuristic algorithms have been improved as clustering based for retinal vessel segmentation and then their performances have been compared to that of well-known Gray Wolf Optimization (Grey Wolf Optimization, GWO) algorithm.

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