Retinal damar segmantasyonuna yönelik yapay arı koloni algoritması tabanlı yaklaşımların performans mukayesesi

Abstract

Retinal kan damarlarında meydana gelen yapısal değişiklikler retinal hastalıklara yönelik önemli bilgiler sağlamaktadır. Bu nedenle, son yıllarda bilgisayar destekli retinal damar segmantasyonu uygulamaları önemli bir araştırma alanı haline gelmiştir. Retinal kan damarları ile retina görüntüsü art alanı arasındaki kontrast farkları çok düşük olduğu için retinal kan damarlarının yüksek doğrulukta tespit edilmesine yönelik güçlü algoritmalara ihtiyaç duyulmaktadır. Bu çalışmada, mühendislik problemlerine etkim çözümler üreten yapay arı koloni (ABC) algoritması retinal damar segmantasyonuna yönelik uygulanmıştır. Retinal kan damarlarının yüksek doğrulukta segmantasyonuna yönelik olarak kümeleme tabanlı ABC (temel ABC), hızlı-ABC (Q-ABC) ve modifiye edilmiş ABC (MR-ABC) algoritmaları geliştirilmiş ve performansları mukayese edilmiştir. Benzetimler, DRIVE veri tabanından alınmış olan normal ve hastalıklı retinal görüntüler üzerinde gerçekleştirilmiştir. Benzetim sonuçları ve istatistiksel analizler ABC tabanlı yaklaşımların kararlı bir şekilde çalıştıklarını ve en uygun kümeleme performanslarına yüksek yakınsama hızlarında ulaştıklarını göstermektedir. Sonuç olarak, ABC tabanlı yaklaşımların retinal kan damarlarının yüksek doğrulukta segmantasyonuna yönelik olarak başarılı bir şekilde kullanılabileceği görülmüştür.

Keywords

• Retinal kan damar segmentasyonu
• yapay arı koloni algoritması
• hızlı yapay arı koloni algoritması
• modifiye edilmiş yapay arı koloni algoritması.

Performance comparison of artificial bee colony algorithm based approaches for retinal vessel segmentation

Abstract

Structural changes in the retinal blood vessels provide important information about retinal diseases. Therefore, computer-aided segmentation of retinal blood vessels has become an active area of research in last decades. Due to the close contrast between the retinal blood vessels and the retinal background, robust methods should be developed to detect retinal blood vessels with high accuracy. In this work, artificial bee colony (ABC) algorithm which provides effective solutions to engineering problems has been applied to the retinal vessel segmentation. Clustering based ABC (basic ABC), quick-ABC (Q-ABC) and modified ABC (MR-ABC) algorithms have been analyzed for accurate segmentation of retinal blood vessels and their performances were compared. The simulations have been realized on the normal and abnormal retinal images taken from the DRIVE database. Simulation results and statistical analyses represent that ABC based approaches are stable and able to reach to optimal clustering performance with higher convergence rates. As a result it can be concluded that ABC based approaches can successfully be used for accurate segmentation of retinal blood vessels.

Keywords

• Retinal blood vessel segmentation
• artificial bee colony algorithm
• quick artificial bee colony algorithm
• modified artificial bee colony algorithm.

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