Original Article International Journal of Fuzzy Logic and Intelligent Systems Vol. 19, No. 4, December 2019, pp. 323-331 http://doi.org/10.5391/IJFIS.2019.19.4.323 ISSN(Print) 1598-2645 ISSN(Online) 2093-744X The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus Wiharto, Esti Suryani, and Murdoko Susilo Department of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia Abstract Blood vessels in the retina of the eye are one important sign when making a diagnosis of hypertensive retinopathy. On the retina can be known several signs including tortuosity and arteriovenous ratio. Blood vessels mixed with a number of objects in the retina, the segmentation of blood vessels becomes a very interesting challenge because they have to separate blood vessels from a number of objects. This study aims to segmentation blood vessels using the main method of self-organizing maps artificial neural networks (SOM- ANN). The proposed segmentation method is divided into three stages, namely preprocessing, segmentation, and performance analysis. The preprocessing step is to improve image quality using the contrast-limited adaptive histogram equalization (CLAHE), median filter, and morphology. The segmentation stage uses the SOM-ANN algorithm combined with the mean or median thresholding. The performance parameters which are measured consist of sensitivity, specificity, and area under the curve (AUC). The test results using the dataset STARE and DRIVE show that the median thresholding is able to provide the best AUC performance compared to the mean thresholding. The proposed segmentation model is able to provide performance in the excellent category, with AUC values of 90.55% for the STARE dataset and 90.35% for the DRIVE. Keywords: Segmentation, Blood vessel, Retinal, SOM-ANN, Thresholding Received: Sep. 16, 2019 Revised : Nov. 30, 2019 Accepted: Dec. 6, 2019 Correspondence to: Wiharto (wiharto@staff.uns.ac.id)) ©The Korean Institute of Intelligent Systems cc This is an Open Access article dis- tributed under the terms of the Creative Commons Attribution Non-Commercial Li- cense (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted non- commercial use, distribution, and reproduc- tion in any medium, provided the original work is properly cited. 1. Introduction Hypertension retinopathy is diagnosed by observing the retina of the eye. Observations were made using a fundus camera. Accuracy in observations is very dependent on the experience of a clinician. The more experienced, the faster and more precise in the diagnosing. The development of information technology has brought changes in health services including in diagnosing the disease [1], one of them is the diagnosis of hypertension retinopathy. This diagnosis is made by analyzing the retinal image produced from the fundus camera. Retinal image analysis is performed using a number of stages, one of which is segmentation. This process is the separation of objects that will be observed for diagnosis with the background. Diagnosis of hypertension retinopathy is done by analyzing blood vessels, so that segmentation is done by separate blood vessels from the background. A number of studies have segmented retinal blood vessels with various methods. The 323 |