Retinal Image Classification for Identification of Cardio Vascular Disease Using Forest Graph

International Journal of Computer Science and Engineering

© 2014 by SSRG - IJCSE Journal

Volume 1 Issue 8

Year of Publication : 2014

Authors : A.Rijuvana Begum, S.Purnima

10.14445/23488387/IJCSE-V1I8P102

How to Cite?

A.Rijuvana Begum, S.Purnima, "Retinal Image Classification for Identification of Cardio Vascular Disease Using Forest Graph," SSRG International Journal of Computer Science and Engineering , vol. 1,  no. 8, pp. 1-5, 2014. Crossref, https://doi.org/10.14445/23488387/IJCSE-V1I8P102

Abstract:

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as hypertension, coronary heart disease, and stroke has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. Accurate measurement of vessel diameters on retinal images plays an important part in diagnosing cardiovascular diseases. In this project, a method of vessel diameter measurement has been developed incorporating with a tracking technique. Vessel edges are then more precisely localized using image profiles computed perpendicularly across a spline fit of each detected vessel centerline, so that both local and global changes in vessel diameter can be readily quantified. The retinal vessel network is used to diagnosis of cardiovascular disease. We use the post processing step to identifying the true vessels from for vascular structure segmentation. So we construct the vessel segment graph and formulate the problem of finding optimal forest in the graph. Using image datasets, we show that the diameters output by our algorithm display good agreement with the manual measurements made by independent observers.

Keywords:

VESSEL SEGMENTATION, GRAPH, VASCULAR STRUCTURE SEGMENTATION.

References:

[1] T.Y.Wong, F. M. A. Islam,R.Klein,B. E.K.Klein,M. F.Cotch,C.Castro, A. R. Sharrett, and E. Shahar, “Retinal vascular caliber, cardiovascular risk factors, and inflammation: The multi-ethnic study of atherosclerosis (MESA),” Invest Ophthalm. Vis. Sci., vol. 47, no. 6, pp. 2341–2350, 2006. 
[2] K. McGeechan, G. Liew, P. Macaskill, L. Irwig, R. Klein, B. E. K. Klein, J. J. Wang, P. Mitchell, J. R. Vingerling, P. T. V. M. Dejong, J. C. M. Witteman,M.M. B. Breteler, J. Shaw, P. Zimmet, and T. Y.Wong, “Metaanalysis: Retinal vessel caliber and risk for coronary heart disease,” Ann.Intern. Med., vol. 151, no. 6, pp. 404–413, 2009. 
[3] C. Y.-L. Cheung, Y. Zheng,W. Hsu, M. L. Lee, Q. P. Lau, P. Mitchell, J. J. Wang, R. Klein, and T. Y. Wong, “Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors,” Ophthalmology, vol. 118, pp. 812– 818, 2011. 
[4] C. Y.-L. Cheung, W. T. Tay, P. Mitchell, J. J. Wang, W. Hsu, M. L. Lee, Q. P. Lau, A. L. Zhu, R. Klein, S. M. Saw, and T. Y. Wong, “Quantitative and qualitative retinal microvascular characteristics and blood pressure,” J. Hypertens, vol. 29, no. 7, pp. 1380–1391, 2011. 
[5] Y. Tolias and S. Panas, “A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering,” IEEE Trans. Med. Imag., vol. 17, no. 2, pp. 263–273, Apr. 1998. 
[6] H. Li, W. Hsu, M. L. Lee, and T. Y. Wong, “Automatic grading of retinal vessel caliber,” IEEE Trans. Biomed. Eng., vol. 52, no. 7, pp. 1352–1355, Jul. 2005. 
[7] E. Grisan, A. Pesce, A. Giani, M. Foracchia, and A. Ruggeri, “A new tracking system for the robust extraction of retinal vessel structure,” in Proc. IEEE Eng. Med. Biol. Soc., Sep. 2004, vol. 1, pp. 1620– 1623. 
[8] Y.Yin,M.Adel, M. Guillaume, and S. Bourennane, “Aprobabilistic based method for tracking vessels in retinal images,” in Proc. IEEE Int. Conf.Image Process., Sep. 2010, pp. 4081–4084. 
[9] M. Martinez-Perez, A. Highes, A. Stanton, S. Thorn, N. Chapman, A. Bharath, and K. Parker, “Retinal vascular tree morphology: A semiautomatic quantification,” IEEE Trans. Biomed. Eng., vol. 49, no. 8, pp. 912–917, Aug. 2002.
[10] H. Azegrouz and E. Trucco, “Max-min central vein detection in retinal fundus images,” in Proc. IEEE Int. Conf. Image Process., Oct. 2006, pp. 1925– 1928.