ANALYSIS OF OPHTHALMIC DISORDERS ON RETINAL VESSELS USING DEEP LEARNING SEGMENTATION METHODS

  • Erke Arıbaş Faculty of Computers and Informatics Engineering, İ.T.Ü, İ.T.Ü Ayazağa Campus, Sarıyer, İstanbul
  • Evren Dağlarlı Faculty of Computers and Informatics Engineering, İ.T.Ü, İ.T.Ü Ayazağa Campus, Sarıyer, İstanbul

Abstract

It’s already been known and acknowledged that manual blood vessel segmentation is a time con-suming and repetitious task. Also, these operation needs training and skill. However, using segmented mechanisms with automated methods in ophthalmic vessels is becoming the crucial and essential feature in the advancement of a computer-supported seg-mentation analyzed scheme especially in detecting vessel related disorders. Separation of retinal arteries are relatively harder to detect since their sizes. Retinal arteries are also like tree structured and allow seam-less automatic segmentation. Segmentation analysis becomes extremely important in detecting any eye diseases especially forming in artery tree connections and itself. In this study, we try to implement deep learning methods in order to complete analysis of these segmentation and compare with other known methods such as Support Vector Machines (SVN). These methods are implemented on the DRIVE da-tabase which contain retinal images and ground truth approval is supplied by medical experts.

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Published
2017-08-16
How to Cite
ARIBAŞ, Erke; DAĞLARLI, Evren. ANALYSIS OF OPHTHALMIC DISORDERS ON RETINAL VESSELS USING DEEP LEARNING SEGMENTATION METHODS. International Journal of Scientific Research in Information Systems and Engineering (IJSRISE), [S.l.], v. 3, n. 2, p. 1-4, aug. 2017. ISSN 2380-5579. Available at: <http://ijsrise.com/index.php/IJSRISE/article/view/81>. Date accessed: 21 nov. 2017.

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