Abstract
Robust image segmentation can be achieved by pixel classification based on features extracted from the image. Retinal vessel quantification is an important component of retinal disease screening protocols. Some vessel parameters are potential biomarkers for the diagnosis of several diseases. Specifically, the arterio-venular ratio (AVR) has been proposed as a biomarker for Diabetic retinopathy and other diseases. Classification of retinal vessel pixels into arteries or veins is required for computing AVR. This chapter compares Extreme Learning Machines (ELM) with other state-of-the-art classifier building approaches for this tasks, finding that ELM approaches improve over most of them in classification accuracy and computational time load. Experiments are performed on a well known benchmark dataset of retinal images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Available online at EIML Group. The op-elm toolbox. http://www.cis.hut.fi/projects/eiml/research/downloads/op-elm-toolbox 2009
P. Bankhead, C.N. Scholfield, J.G. McGeown, T.M. Curtis, Fast retinal vessel detection and measurement using wavelets and edge location refinement. PloS one 7(3), e32435 (2012)
M.A. David, The relationship between variable selection and data augmentation and a method for prediction. Technometrics 16(1), 125–127 (1974)
M.M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, S.A. Barman, Blood vessel segmentation methodologies in retinal images-a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012)
Guangbin Huang. ELM Homepage. http://www.ntu.edu.sg/home/egbhuang/elm_codes.html 2012
A.E. Hoerl, Application of ridge analysis to regression problems. Chem. Eng. Prog. 58, 54–59 (1962)
G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
G.-B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. 42(2), 513–529 (2012)
D. Larry, D.L. Hubbard, R.J. Brothers, W.N. King, L.X. Clegg, R. Klein, L.S. Cooper, A.R. Sharrett, M.D. Davis, J. Cai, Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study. Ophthalmology 106(12), 2269–2280 (1999)
G. Liew, J.J. Wang, P. Mitchell, T.Y. Wong. Retinal vascular imaging a new tool in microvascular disease research. Circ. Cardiovasc. Imaging 1(2), 156–161 (2008)
G. Liew, J.J. Wang, Retinal vascular signs: a window to the heart? Revista Española de Cardiología (English Edition) 64(6), 515–521 (2011)
Y. Miche, A. Sorjamaa, P. Bas, O. Simula, C. Jutten, A. Lendasse, Op-elm: Optimally pruned extreme learning machine. IEEE Trans. Neural Networks 21(1), 158–162 (2010)
M. Niemeijer, B. van Ginneken, M.D. Abràmoff, Automatic classification of retinal vessels into arteries and veins. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 72601F (2009)
E. Ricci, R. Perfetti, Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Medical Imaging 26(10), 1357–1365 (2007)
M. Saez, G-V. Sonia, G-P. Manuel, M.A. Barceló, P-S. Marta, G. Coll de Tuero, P-R. Antonio, Development of an automated system to classify retinal vessels into arteries and veins. Comput. Methods Programs Biomed. 108(1), 367–376 (2012)
T.T. Thanh, T.Y. Wong, Retinal vascular manifestations of metabolic disorders. Trends Endocrinol. Metab. 17(7), 262 (2006)
A. Tikhonov, Solution of inc orrectly formulated problems and the regularization method. Sov. Math. Doklady 5, 1035–1038 (1963)
K.-A. Toh, Deterministic neural classification. Neural Comput. 20(6), 1565–1595 (2008)
A. Zamperini, A. Giachetti, E. Trucco, K.S. Chin, Effective features for artery-vein classification in digital fundus images. 25th International Symposium on Computer-Based Medical Systems (CBMS), 1–6 (2012)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Barandiaran, I., Maiz, O., Marqués, I., Ugarte, J., Graña, M. (2014). ELM for Retinal Vessel Classification. In: Sun, F., Toh, KA., Romay, M., Mao, K. (eds) Extreme Learning Machines 2013: Algorithms and Applications. Adaptation, Learning, and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-04741-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-04741-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04740-9
Online ISBN: 978-3-319-04741-6
eBook Packages: EngineeringEngineering (R0)