Deteksi Pembuluh Darah pada Citra Fundus Retina Menggunakan Gabungan Metode Segementasi Pembuluh Darah Lebar dan Tipis

Organs in the human body provide a lot of knowledge about human body health. One of them is the organ in the human eye, more specifically the retina.Furthermoreeven some accute diseases can be detected through the retina image. One of the diseases that can be detected through retinal images is diabe...

Full description

Saved in:
Bibliographic Details
Published inJurnal Teknologi Informasi dan Terapan Vol. 9; no. 1
Main Authors Agustianto, Khafidurrohman, Choirunnisa, Shabrina, Afianah, Nuzula, Huda, Choirul
Format Journal Article
LanguageEnglish
Published 27.06.2022
Online AccessGet full text

Cover

Loading…
More Information
Summary:Organs in the human body provide a lot of knowledge about human body health. One of them is the organ in the human eye, more specifically the retina.Furthermoreeven some accute diseases can be detected through the retina image. One of the diseases that can be detected through retinal images is diabetic retinopathy. This disease can be identified through segmentation to find abnormalities in the retinal blood vessels. These abnormalities include enlarged blood vessels, abnormal branching, and so on. Manual detection of blood vessels on the retina is less accurate, so a comperhensive program is required in order to segment the blood vessels in eye fundus images, more accurately. In this research, some method and algorithms are approached by combining the segmentation process for wide vessels and the segmentation process for thin vessels. There are three stages in this research. First, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and 2D Gabor Wavelet are applied as the preprocessing stage. Then, active contour and region growing methods are required as the processing to segment the thin and wide vessels in fundus retina image. And the last stage is postprocessing using the logical operator method or. The trial in this research uses color eye fundus images on the DRIVE dataset consisting of 20 retinal photos. By using this dataset, the average accuracy is 94.32%, sensitivity is 77.09% and specificity is 96.04% in 20 trials.
ISSN:2354-838X
2580-2291
DOI:10.25047/jtit.v9i1.276