CRTI - Centre de Recherche en Technologie de l'Information
Disciplines :
Computer science
Author, co-author :
Lazouni, Mohammed Amine
Feroui, Amel
Mahmoudi, Said ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
A new intelligent system for glaucoma disease detection
Publication date :
29 March 2019
Journal title :
International Journal of Computer Aided Engineering and Technology
ISSN :
1757-2657
Publisher :
Inderscience Publishers, Switzerland
Volume :
11
Issue :
4/5
Pages :
613 - 633
Peer reviewed :
Peer Reviewed verified by ORBi
Research unit :
F114 - Informatique, Logiciel et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
Abidi, S.S., Artes, P.H., Yun, S. and Yu, J. (2007) 'Automated interpretation of optic nerve images: a data mining framework for glaucoma diagnostic support', Stud. Health Technol. Inform., Vol. 129, No. 2, pp.1309-1313.
Ali, M.A., Hurtut, T., Faucon, T. and Cheriet, F. (2014) 'Glaucoma detection based on local binary patterns in fundus photographs', SPIE Medical Imaging. Vol. 7, pp.903531-903531, USA.
Allouch, C., Touzeau, O., Borderie, V., Fogel, S., Zito, E. and Laroche, L. (2004) 'Analyse biométrique par Orbscan et échographie du glaucome aigu par fermeture de l'angle', Journal Français d'Ophtalmologie, Vol. 27, No. 3, pp.239-248.
Allouch, C., Touzeau, O., Borderie, V., Fogel, S., Zito, E. and Laroche, L. (2004) 'Analyse biométrique par Orbscan et échographie du glaucome aigu par fermeture de l'angle', Journal Francais D'Ophtalmologie, Vol. 27, No. 3, pp.239-248.
Almazroa, A., Burman, R., Raahemifar, K. and Lakshminarayanan, V. (2015) 'Optic disc and optic cup segmentation methodologies for glaucoma image detection', Journal of Ophthalmology, Vol. 2015, p.2. Article ID 180972.
Bernatavičienė, J., Dzemyda, G., Kurasova, O., Barzdžiukas, V., Buteikienė, D. and Paunksnis, A. (2008) 'Rule induction for ophthalmological data classification', The 20th International Conference, EurOPT Mini Conference, pp.328-334.
Bessaid, A., Feroui, A. and Messadi, M. (2009) 'Detection of blood vessels from retinal images using watershed transformation', Journal of Mechanics in Medicine and Biology, Vol. 9, No. 4, pp.633-642.
Brown, F. and Fletcher, R. (1990) 'Historical background and evolution of primary health care', Modern Optometry. Glaucoma in Optometric Practice, pp.1-17, Blackwell Scientific Publications, Oxford.
Chauhan, K., Chauhan, P., Sudhalkar, A., Lad, K. and Gulati, R. (2016) 'Data mining techniques for diagnostic support of glaucoma using stratus OCT and perimetric data', International Journal of Computer Applications, Vol. 151, No. 8, pp.0975-8887.
Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D.K., Tan, N.M., Tao, D., Cheng, C.Y., Aung, T. and Wong, T.Y. (2013) 'Superpixel classification based optic disc and optic cup segmentation for glaucoma screening', IEEE Trans Medical Imaging, Vol. 32, No. 6, pp.1019-1032.
Cheng, J., Yin, F., Wong, D.W., Tao, D. and Liu, J. (2015) 'Sparse dissimilarity-constrained coding for glaucoma screening', IEEE Transactions on Biomedical Engineering, Vol. 62, No. 5, pp.1395-403.
Claro, M., Santos, L., Araujo, W.F. and Moura, N. (2016) 'Automatic glaucoma detection based on optic disc segmentation and texture feature extraction', CLEI Electronic Journal, Vol. 19, No. 2, pp.1-10.
Daho, M.E.H. and Chikh, M.A. (2014) Classification And Recognition Of Biomedical Data With Ensemble Methods, Doctoral Thesis, Abou Bekr Belkaid Tlemcen University.
Dey, A. and Bandyopadhyay, S.K. (2016) 'Automated glaucoma detection using support vector machine classification method', British Journal of Medicine & Medical Research, Vol. 11, No. 12, pp.1-12.
Fitzgibbon, A., Pilu, M. and Fisher, R.B. (1999) 'Direct least square fitting of ellipses', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 5, pp.476-480.
Kira, K. and Rendell, L.A. (1992) 'A practical approach to feature selection', The 9th International Workshop on Machine Learning, pp.249-256, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
Kononenko, I. (1994) 'Estimating attributes: analysis and extensions of relief', The European Conference on Machine Learning, Vol. 784, pp.21-46, ISBN 3-540-57868-4.
Lotankar, M.L., Noronha, K. and Koti, J. (2015) 'Glaucoma screening using digital fundus image through optic disc and cup segmentation', International Journal of Computer Applications, Vol. 6, pp.24-29.
Meyer, F. and Beucher, S. (1990) 'Morphological segmentation', Journal of Visual Communication and Image Representation, Vol. 1, No. 1, pp.21-46.
Murthi, A. and Madheswaran, M. (2012) 'Enhancement of optic cup to disc ratio detection in glaucoma diagnosis', International Conference on Computer Communication and Informatics, pp.1-5.
National Eye Institute (2015) Facts About Glaucoma [online] http://www.nei.nih.gov/health/glaucoma/glaucoma_facts.asp (accessed 11 February 2013).
National Institutes of Health (2017) Medline Plus. Glaucoma [online] http://www.nlm.nih.gov/medlineplus/ency/article/001620.htm (accessed 11 February 2013).
Online Glaucoma Research Foundation (2007/2010) Glaucoma Can Strike at All Ages, Even Newborn Babies [online] http://www.glaucoma.org/gleams/glaucoma-can-strike-at-all-ageseven-newborn-babies.php (accessed February 2017).
Online Glaucoma Research Foundation (2010) Types of Glaucoma [online] http://www.glaucoma.org/glaucoma/types-of-glaucoma.php, (accessed August 2016).
Online Glaucoma Research Foundation (2011) 'Are You at Risk For Glaucoma? Last reviewed on January 13, 2011 Compared with Patients who Have a Second Type which Primary Open-Angle Glaucoma [online] http://www.glaucoma.org.
Online Glaucoma Research Foundation (2013) Five Common Glaucoma Tests, April[online] http://www.glaucoma.org/glaucoma/diagnostic-tests.php (accessed 11 February 2013).
Parikh, K.S. and Shah, T.P. (2016) 'Support vector machine - a large margin classifier to diagnose skin illnesses', 3rd International Conference on Innovations in Automation and Mechatronics Engineering, Vol. 23, pp.369-375.
Quigley, H.A. and Broman, A.T. (2006) 'The number of people with glaucoma worldwide in 2010 and 2020', British Journal of Ophthalmology, Vol. 90, No. 3, pp.262-267.
Sakthivel, K. and Narayanan, R. (2015) 'An automated detection of glaucoma using histogram features', International Journal of Ophtalmology, Vol. 8, No. 1, pp.194-200.
Salam, A.A., Khalil, M.T., Akram, U., Jameel, A. and Basit, A. (2016) 'Automated detection of glaucoma using structural and non structural features', Springerplus, Vol. 5, No. 1519, pp.3-21.
Sherpa, D. and Badhu, B.P. (2008) 'Association between axial length of the eye and primary angle closure glaucoma', Kathmandu University Medical Journal (KUMJ), Vol. 6, No. 23, pp.361-363.
Soille, P. (1999) Morphological Image Analysis, Principles and Applications, 2nd ed., Vol. 1, Springer-Verlag Berlin, Heidelberg, New York.
Thulasi, N. (2013) 'Diagnosis of glaucoma using haralick texture features', Journal of Computer Engineering, Vol. 15, No. 1, pp.12-17.
Thylefors, B. and Negrel, A.D. (1994) 'The global impact of glaucoma', Bull World Health Organization, Vol. 72, No. 3, pp.323-326.
Vincent, L. (1993) 'Morphological grayscale reconstruction in image analysis: applications and efficient algorithms', IEEE Transaction on Image Processing, Vol. 2, No. 2, pp.176-201.
Yun, S.C., Hahn, I.K., Sung, K.R., Yoon, J.Y., Jeong, D. and Chung, H.S. (2015) 'Lamina cribrosa depth according to the level of axial length in normal and glaucomatous eyes', Graefe's Archive for Clinical and Experimental Ophthalmology, Vol. 253, No. 12, pp.2247-2253.
Zhang, Y.J. (2002) 'Image engineering and related publications', Int. J. Image Gr., Vol. 2, No. 3, pp.441-452.
