Abstract
This paper describes a new supervised method for segmentation of blood vessels in retinal images of multi ethnic school children. This method uses an ensemble classification system of boot strapped decision trees. A filter bank of the dual Gaussian and the Gabor filters, along with the line strength measure of blood vessels is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with strong light reflexes along their centerline, which is more apparent on arteriolars than venules, and in children compared to adult patients. For this purpose we also present a new public retinal image database of multi ethnic school children along with vessel segmentation ground truths. The image set is named as CHASE_DB1 and is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system for vessel segmentation is evaluated on CHASE_DB1 in detail, and the incurred accuracy, speed, robustness and simplicity make the algorithm a suitable tool for automated retinal image analysis in large population based studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wong, T.Y., Klein, R., Sharrett, A.R., Duncan, B.B., Couper, D.J., Tielsch, J.M., Klein, B.E.K., Hubbard, L.D.: Retinal Arteriolar Narrowing and Risk of Coronary Heart Disease in Men and Women. JAMA: The Journal of the American Medical Association 287, 1153–1159 (2002), http://jama.ama-assn.org/content/1287/1159/1153.abstract
Gelman, R., Martinez-Perez, M.E., Vanderveen, D.K., Moskowitz, A., Fulton, A.B.: Diagnosis of Plus Disease in Retinopathy of Prematurity Using Retinal Image multiScale Analysis. Investigative Ophthalmology & Visual Science 46, 4734–4738 (2005)
Owen, C.G., Rudnicka, A.R., Nightingale, C.M., Mullen, R., Barman, S.A., Sattar, N., Cook, D.G., Whincup, P.H.: Retinal Arteriolar Tortuosity and Cardiovascular Risk Factors in a Multi-Ethnic Population Study of 10-Year-Old Children; the Child Heart and Health Study in England (CHASE). Arteriosclerosis, Thrombosis, and Vascular Biology (2011)
Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 19, 203–210 (2000)
Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 25, 1200–1213 (2006)
Quek, F.K.H., Kirbas, C.: Vessel extraction in medical images by wave-propagation and traceback. IEEE Transactions on Medical Imaging 20, 117–131 (2001)
Palomera-Perez, M.A., Martinez-Perez, M.E., Benitez-Perez, H., Ortega-Arjona, J.L.: Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection. IEEE Transactions on Information Technology in Biomedicine 14, 500–506 (2010)
Li, W., Bhalerao, A., Wilson, R.: Analysis of Retinal Vasculature Using a Multiresolution Hermite Model. IEEE Transactions on Medical Imaging 26, 137–152 (2007)
Al-Diri, B., Hunter, A., Steel, D.: An Active Contour Model for Segmenting and Measuring Retinal Vessels. IEEE Transactions on Medical Imaging 28, 1488–1497 (2009)
Gooya, A., Hongen, L., Matsumiya, K., Masamune, K., Masutani, Y., Dohi, T.: A Variational Method for Geometric Regularization of Vascular Segmentation in Medical Images. IEEE Transactions on Image Processing 17, 1295–1312 (2008)
Nekovei, R., Ying, S.: Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Transactions on Neural Networks 6, 64–72 (1995)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 501–509 (2004)
Soares, J.V.B., Leandro, J.J.G., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging 25, 1214–1222 (2006)
Ricci, E., Perfetti, R.: Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Transactions on Medical Imaging 26, 1357–1365 (2007)
Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: Retinal Vessel Segmentation Using AdaBoost. IEEE Transactions on Information Technology in Biomedicine 14, 1267–1274 (2010)
Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Transactions on Medical Imaging 30, 146–158 (2011)
CHASE_DB1 (2011), http://sec.kingston.ac.uk/retinal
ARIA Online; Retinal Image Archive (2006), http://www.eyecharity.com/aria_online/
Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Pietilä, J., Kälviäinen, H., Uusitalo, H.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Medical Image Understanding and Analysis (MIUA 2007), Aberystwyth, Wales, UK, pp. 61–65 (2007)
MESSIDOR: Methods for Evaluating Segmentation and Indexing techniques Dedicated to Retinal Ophthalmology (2004), http://messidor.crihan.fr/index-en.php
Al-Diri, B., Hunter, A., Steel, D., Habib, M., Hudaib, T., Berry, S.: REVIEW - A reference data set for retinal vessel profiles. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 2262–2265 (2008)
Niemeijer, M., van Ginneken, B., Cree, M.J., Mizutani, A., Quellec, G., Sanchez, C.I., Zhang, B., Hornero, R., Lamard, M., Muramatsu, C., Wu, X., Cazuguel, G., You, J., Mayo, A., Qin, L., Hatanaka, Y., Cochener, B., Roux, C., Karray, F., Garcia, M., Fujita, H., Abramoff, M.D.: Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging 29, 185–195 (2009)
Owen, C.G., Rudnicka, A.R., Mullen, R., Barman, S.A., Monekosso, D., Whincup, P.H., Ng, J., Paterson, C.: Measuring Retinal Vessel Tortuosity in 10-Year-Old Children: Validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) Program. Investigative Ophthalmology & Visual Science 50, 2004–2010 (2009)
Kelvin, P., Ghassan, H., Rafeef, A.: Live-Vessel: Extending Livewire for Simultaneous Extraction of Optimal Medial and Boundary Paths in Vascular Images. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 444–451. Springer, Heidelberg (2007)
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Kennedy, R.L.: Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Transactions on Medical Imaging 23, 1196–1204 (2004)
Movellan, J.R.: Tutorial on Gabor Filters. Tutorial paper (2008), http://mplab.ucsd.edu/tutorials/pdfs/gabor.pdf
Zwiggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.: Linear structures in mammographic images: detection and classification. IEEE Transactions on Medical Imaging 23, 1077–1086 (2004)
Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 6, 21–45 (2006)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fraz, M.M. et al. (2012). Ensemble Classification System Applied for Retinal Vessel Segmentation on Child Images Containing Various Vessel Profiles. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-31298-4_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31297-7
Online ISBN: 978-3-642-31298-4
eBook Packages: Computer ScienceComputer Science (R0)