SEGMENTASI CITRA PEMBULUH DARAH RETINA MENGGUNAKAN METODE DETEKSI GARIS MULTI SKALA
Abstract
Changes in retinal blood vessels feature a sign of serious illnesses such as heart disease and stroke. Therefore, the analysis of retinal vascular features can help in detecting these changes and allow patients to take preventive measures at an early stage of this disease. Automation of this process will help reduce the costs associated with the specialist and eliminate inconsistencies that occur in manual detection system. Among the retinal image analysis, image extraction retinal blood vessels is a crucial step before measurement. In this paper, we use an effective method of automatically extracting the blood vessels of the color images of the retina using a length detector line in several different scales, in order to maintain the strength and eliminates the weaknesses of each detector individual lines, the result of the detection lines on various scales combined to produce a segmentation of each image of the retina. The performance of the method is evaluated quantitatively using DRIVE dataset. Test results show that this method achieve high accuracy is 0.9407 approaching measurement results manually by experts, and this method produces accurate segmentation in detecting retinal blood vessels with effciency by quickly segmenting time is 2.5 seconds per image.
Downloads
References
[2]. T. Y. Wong, R. McIntosh, Hypertensive retinopathy signs as risk indicators of cardiovascular morbidity and mortality, British medical bulletin 73 (1) (2005) 57-70.
[3]. E. J. Sussman, W. G. Tsiaras, K. A. Soper, Diagnosis of diabetic eye disease, JAMA: the journal of the American Medical Association 247 (23) (1982) 3231-3234.
[4]. T. Y. Wong, R. Klein, D. J. Couper, L. S. Cooper, E. Shahar, L. D. Hubbard, M. R.Wo_ord, A. R. Sharrett, Retinal microvascular abnormalities and incident stroke: the atherosclerosis risk in communities study, The Lancet 358 (9288) (2001) 1134-1140.
[5]. T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, B. E. K. Klein, L. D. Hubbard, F. J. Nieto, Retinal arteriolar diameter and risk for hypertension, Annals of internal medicine 140 (4) (2004) 248-255.
[6]. J. Staal, M. D. Abrmo_, M. Niemeijer, M. A. Viergever, B. van Ginneken, Ridge-based vessel segmentation in color images of the retina, Medical Imaging, IEEE Transactions on 23 (4) (2004) 501-509.
[7]. J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek, M. J. Cree, Retinal vessel segmentation using the 2-d gabor wavelet and supervised classi_cation, Medical Imaging, IEEE Transactions on 25 (9) (2006) 1214-1222.
[8]. E. Ricci, R. Perfetti, Retinal blood vessel segmentation using line operators and support vector classi_ cation, Medical Imaging, IEEE Transactions on 26 (10) (2007) 1357-1365.
[9]. K. Fritzsche, A. Can, H. Shen, C. Tsai, J. Turner, H.L.Tanenbuam, C.V. Stewart, B. Roysam, J.S. Suri, S.Laxminarayan, Automated model based segmentation,tracing and analysis of retinal vasculature from digital fundus images, in: State-of-TheArt Angiography, Applications and Plaque Imaging Using MR, CT Ultrasound and X-rays, Academic Press, 2003, pp. 225–298.
[10]. E. Ricci, R. Perfetti, Retinal blood vessel segmentation using line operators and support vector classication, Medical Imaging, IEEE Transactions on 26 (10) (2007) 1357-1365.
[11]. Uyen T. V. Nguyen, Alauddin Bhuiyan, Laurence A. F. Park, Kotagiri Ramamohanarao, An Efective Retinal Blood Vessel Segmentation Method using Multi-scale Line Detection, article in pattern recognation – ResearchGate. On 1 (2012).
.png)
