Color Feature Segmentation Image for Identification of Cotton Wool Spots on Diabetic Retinopathy Fundus

Pranata, Feriantano Sundang and Na’am, Jufriadif and Hidayat, Rahmat (2020) Color Feature Segmentation Image for Identification of Cotton Wool Spots on Diabetic Retinopathy Fundus. International Journal on Advanced Science, Engineering and Information Technology, 10 (3). pp. 974-979. ISSN 2088-5334

Color feature segmentation image for identification of cotton wool spots on diabetic retinopathy fundus.pdf

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Fundus is an image of the inner eye surface in the form of a colored image. This image has a lot of pixel values because it consists of three basic color components. The three colors are red, green, and blue, so they need a good technique in analyzing this image. This image can be used to diagnose diabetic retinal disease caused by diabetes mellitus. This disease can interfere with human vision because objects that cover the retina of the eye is called Cotton Wool Spot (CWS). The severity of this disease can be observed from the large area of the CWS covering the retina. This study aims to calculate the exact area ratio of CWS with the retina area. The method used in this research is Image Color Feature Segmentation (ICFS). This method has four stages, namely preprocessing, segmentation, feature extraction, and feature areas. The dataset processed in this study was sourced from the Radiology Department, General Hospital of M. Djamil Padang. The dataset consists of 16 fundus images of patients who were treated at the hospital. The results of this study can identify and calculate the percentage of retinal damage is very well. Therefore, this study can be a reference in measuring the severity of diabetic retinopathy for prevention and subsequent treatment for patients and doctors. Keywords— diabetic retinopathy; Cotton Wool Spot (CWS); fundus image; feature region; pixel area.

Item Type: Article
Subjects: 0 Research > Ilmu Komputer > Image Processing
0 Research > Ilmu Komputer > Teknologi Kesehatan
Divisions/ Fakultas/ Prodi: Fakultas Ilmu Komputer
Depositing User: Administrator
Date Deposited: 27 Aug 2021 06:25
Last Modified: 27 Aug 2021 06:25

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