Multispectral Retinal Blood Vessel Analysis for Detecting Eye Diseases

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The proposed work on the automatic multispectral retinal blood vessel analysis for detecting eye diseases uses the Discrete Wavelet Transform and Local Binary Patterns for feature extraction procedure. The sub-bands of the Discrete Wavelet Transform provide the horizontal, vertical and diagonal details of the image under analysis at different frequency levels. The resulting horizontal, vertical and diagonal sub-bands are then given to the Local Binary Patterns module that generates the local features of the directional wavelet sub-bands. The classification in healthy and glaucomatous images is done using Support Vector Machines. The obtained results show better performance than state of the art algorithms found in the literature for the High-Resolution Fundus image database.
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Discrete Wavelet Transform; Local Binary Patterns; Support Vector Machines; Retina; Multispectral

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I. Daubechies, "Orthornomal bases of compactly supported wavelets," Communications on pure and applied mathematics, Vol. 41, No. 7, 1998, pp. 909-996.

S. Mallat, “A wavelet tour of signal processing” Academic press, 1999.

T. Ojala, M. Pietikainen and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Transactions on pattern analysis and machine intelligence, Vol. 24, No. 7, 2002, pp. 971-987.

T. Ahonen, A. Hadid and M. Pietikainen, “Face description with local binary patterns: application to face recognition”, IEEE transactions on pattern analysis and machine intelligence, Vol. 28, No. 12, 2006, pp. 2037-2041.

C. Kondermann, D. Kondermann and M. Yan, “Blood vessel classification into arteries and veins in retinal images” Medical Imaging, 2007, pp. 651247-651247, International Society for Optics and Photonics.

R. Klein, B. E. Klein and S. E. Moss, “The relation of systemic hypertension to changes in the retinal vasculature: the Beaver Dam Eye Study” Transactions of the American Ophthalmological Society, Vol. 95:329, 1997.

A. Budai, G Michelson and J. Hornegger, “Multiscale Blood Vessel Segmentation in Retinal Fundus Images” Bildverarbeitung für die Medizin, March 2010, pp. 261-265.

Gao, X., Retinal Vessel Segmentation Using Multi-Scale Line Detection, (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 613-619.

Benadict Raja, J., Ravichandran, C., Fast Localization of the Optic Disc in Retinal Images Using Intensity and Vascular Information, (2014) International Review on Computers and Software (IRECOS), 9 (7), pp. 1282-1292.

Karthikeyan, S., Rengarajan, N., Hybrid Feature Analysis for Assessment of Glaucoma Using RNFL Defects, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 178-187.

D. S. Raja, S. Vasuki, D. R. Kumar, “Performance analysis of retinal image blood vessel segmentation”, Advanced Computing. Vol. 5, No. 2/3, 2014, pp. 17-23.

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach” IEEE Transactions on Medical Imaging. Vol. 30, No. 6, 2011, pp. 1184-1191.

H. Betaouaf and A. Bessaid, “A biometric identification algorithm based on retinal blood vessels segmentation using watershed transformation”, 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), May 2013, pp. 256-261, IEEE.

O. Nafea O, S. Ghouzali, W. Abdul and E. Qazi, “Hybrid Multi-Biometric Template Protection Using Watermarking”, The Computer Journal, Vol. 59, No. 9, 2016, pp. 1192-1407.

W. Abdul, “Securing Biometric Authentication Through Multimodal Watermarking”, Artificial Intelligence, Modelling and Simulation (AIMS), 2015 3rd International Conference on 2015 Dec 2, pp. 343-346, IEEE.

S. Ghouzali, M. Lafkih, W. Abdul, M. Mikram, M. El Haziti and D. Aboutajdine, “Trace attack against biometric mobile applications”, Mobile Information Systems, Vol. 2016, 2016.

High-Resolution Fundus (HRF) image database - 26-02-2017.

A. F. Frangi, W. J. Niessen, K. L. Vincken and M. A. Viergever, “Multiscale vessel enhancement filtering” International Conference on Medical Image Computing and Computer-Assisted Intervention, 1998, Oct 11, pp. 130-137, Springer Berlin Heidelberg.

A. Budai, R. Bock, A. Maier, J. Hornegger and G. Michelson, “Robust vessel segmentation in fundus images”, International journal of biomedical imaging, Vol. 2013, 2013.

A. Budai, J. Hornegger and G. Michelson, “Multiscale Approach for Blood Vessel Segmentation on Retinal Fundus Images”, Investigative Ophthalmology & Visual Science, 2009, 50: E-Abstract 325, 2009.

J. Odstrcilik, J. Jan, R. Kolar and J. Gazarek, “Improvement of vessel segmentation by matched filtering in colour retinal images”, IFMBE Proceedings of World Congress on Medical Physics and Biomedical Engineering, 2009, pp. 327 - 330.

A. Budai, J. Odstricilik, R. Kollar, J. Jan, T. Kubena and G. Michelson, “A Public Database for the Evaluation of Fundus Image Segmentation Algorithms”, Poster at Fort Lauderdale Convention Center, The Association of Research in Vision and Ophthalmology (ARVO) Annual Meeting in Fort Lauderdale, FL, USA

J. Odstrcilik, R. Kolar, A. Budai, J. Hornegger, J. Jan, J. Gazarek, T. Kubena, P. Cernosek, O. Svoboda, E. Angelopoulou, “Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database”, IET Image Processing, Vol. 7, No. 4, 2013, pp. 373-383.


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