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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ
Dite Geovani
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Computer Science & IT Engineering Other

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Journal : ComEngApp : Computer Engineering and Applications Journal

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The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images Cahyo Pambudi Darmo; Lucky Indra Kesuma; Dite Geovani
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.452

Abstract

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.
Co-Authors Anita Desiani Aulia Salsabila Bambang Suprihatin Darmo, Cahyo Pambudi Ira Rayyani Kesuma, Lucky Indra Nyayu Chika Marselina Redina An Fadhila Chaniago Rifkie Primartha Sri Indra Maiyanti Sugandi Yahdin