Platform image processing to study the structural properties of retinal vessel

General information

Authors:
Gabino VERDE, Luis GARCÍA-ORTIZ, Carolina ZATO, Juan Francisco DE PAZ, Sara RODRÍGUEZ, Miguel Ángel MERCHÁN

DOI:
10.14201/ADCAIJ2013245559

Volume:
Regular Issue 2 (1), 2013

Keywords:

Arterial stiffness; Cardiovascular disease; AI algorithms; Pattern recognition; Image analysis

This paper presents a technological platform specialized in assessing retinal vessel caliber and describing the relationship of the results obtained to cardiovascular risk. Retinal circulation is an area of active research by numerous groups, and there is general experimental agreement on the analysis of the patterns of the retinal blood vessels in the normal human retina. The development of automated tools designed to improve performance and decrease interobserver variability, therefore, appears necessary.

References

Akita, K., Kuga. H. A computer method of understanding ocular fundus images. Pattern Recogn., 16 (1982), pp. 431–443

Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M., 1989a. Automatic detection of the optic nerve in retinal images. In: Proceedings of the IEEE International Conference on Image Processing, vol. 1. Singapore, pp. 1–5.

Chen, B., C. Tosha, M.B. Gorin, S. Nusinowitz. Analysis of Autofluorescent retinal images and measurement of atrophic lesion growth in Stargardt disease. Experimental Eye Research, Volume 91, Issue 2, August 2010, Pages 143-152

De Paz J.F., Rodríguez S., Bajo J., Corchado J.M. CBR System for Diagnosis of Patient. Pags.: 807-812 pags. Editorial / Publisher: IEEE Computer Society Press. Proceedings of HIS 2008. ISBN: 978-0-7695-3326-1. 2009

García-Ortiz, José I. Recio-Rodríguez, Javier Parra-Sanchez, Luis J. González Elena, María C. Patino-Alonso, Cristina Agudo-Conde, Emiliano Rodríguez-Sánchez, Manuel A. Gómez-Marcos, on behalf of the Vaso-risk group- A new tool to assess retinal vessel caliber. Reliability and validity of measures and their relationship with car-diovascular risk. www.jhypertension.com. Volume 30 Number . April 2012

Goldbaum, M. Katz, N. , Nelson, M., Haff L.The discrimination of similarly colored objects in computer images of the ocular fundus. Invest. Ophthalmol. Vis. Sci., 31 (1990), pp. 617–623

Heneghan, C., J. Flynn, M. O’Keefe, M. Cahill. Characterization of changes in blood vessel and tortuosity in retinopathy of prematurity using image analysis. Med. Image Anal., 6 (2002), pp. 407–429

Heras, E., F. De la Prieta, V. Julian, S. Rodríguez, V. Botti, J. Bajo and J.M. Corchado. Agreetment tech-nologies and their use in cloud computing environments. In: Progress in Artificial Intelligence. Volume 1. Number 4. (2012).

Hoover, A., Goldbaum, M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Biomed. Eng., 22 (2003), pp. 951–958

Hoover, A., Kouznetsoza, V., Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response.IEEE Trans. Med. Imag., 19 (2000), pp. 203–210

Hunter, A., Lowell, J., Steel, D., Basu, A., Ryder, R., 2002. Non-linear filtering for vascular segmentation and detection of venous beading. University of Durham.

Jagoe Roger, J. Arnold, C. Blauth, P.L.C. Smith, K.M. Taylor, R. Wootton. Measurement of capillary drop-out in retinal angiograms by computerised image analysis. Pattern Recognition Letters, Volume 13, Issue 2, February 1992, Pages 143-151.

Kalviainen, H., Hirvonen, P., Xu, L., Oja E. Probabilistic and non-probabilistic Hough transforms. Image Vision Comput., 13 (1995), pp. 239–252

Lee, S. ,Wang, Y., Lee E., A computer algorithm for automated detection and quantification of microaneu-rysms and haemorrhages in color retinal images.SPIE Conference on Image Perception and Performance, vol. 3663 (1999), pp. 61–71

Li,H., Chutatape, O. Automated feature extraction in color retinal images by a model based approach IEEE Trans. Biomed. Eng., 51 (2004), pp. 246–254

Lowell, J. A. Hunter, D. Steel, A. Basu, R. Ryder, L. Kennedy. Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Trans. Biomed. Eng., 23 (2004), pp. 1196–1204

Patton, Niall,Tariq M. Aslam, Thomas MacGillivray, Ian J. Deary, Baljean Dhillon, Robert H. Eikelboom, g, Kanagasingam Yogesan, Ian J. Constable (2006) Retinal image analysis: Concepts, applications and potential. Progress in Retinal and Eye Research.Elsevier.Volume 25, Issue 1, January 2006, Pages 99–127

Rodríguez S., De Paz J.F., Bajo J. and Corchado J.M. Applying CBR Sytems to Micro-Array Data Classifi-cation. Springer Velag. Advances in Soft Computing Series. Proceedings of IWPACBB 2008. ISBN: 978-3-540-85860-7. 2010

Sánchez, C., Hornero, R., López, M.I., Aboy, M., Poza, J., Abásolo, D. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Medical Engineering & Physics, Volume 30, Issue 3, April 2008, Pages 350-357

Tamura, S., Okamoto, Y., Yanashima, K. Zero-crossing interval correction in tracing eye-fundus blood ves-sels Pattern Recogn., 21 (1988), pp. 227–233

Tanabe Y, Kawasaki R, Wang JJ, Wong TY, Mitchell P, Daimon M, et al. Retinal arteriolar narrowing pre-dicts 5-year risk of hypertension in Japanese people: the Funagata study. Microcirculation 2010; 17:94–102.

Wong TY, Duncan BB, Golden SH, Klein R, Couper DJ, Klein BE, et al. Associations between the metabolic syndrome and retinal microvascular signs: the Atherosclerosis Risk In Communities study. Invest Ophthalmol Vis Sci 2004; 45:2949–2954.

Wong TY, Klein R, Sharrett AR, Duncan BB, Couper DJ, Tielsch JM, et al. Retinal arteriolar narrowing and risk of coronary heart disease in men and women. The Atherosclerosis Risk in Communities Study. JAMA 2002; 287:1153–1159.

Yatsuya H, Folsom AR, Wong TY, Klein R, Klein BE, Sharrett AR. Retinal microvascular abnormalities and risk of lacunar stroke: Atherosclerosis Risk in Communities Study. Stroke 2010; 41:1349–1355.

How to cite item?