MICRO STATISTICAL DESCRIPTORS FOR GLAUCOMA DIAGNOSIS USING NEURAL NETWORKS

  • Mukil Alagirisamy
Keywords: Glaucoma diagnosis, fundus image, micro textures, computer aided diagnosis, LVQ-ANN.

Abstract

A fully automatic Computer Aided diagnosis (CAD) of glaucoma is developed that aims to reduce the false positive detection rate and increasing the sensitivity of classification. It consists of three main steps: Region of Interest (ROI) extraction (Optic Disc (OD) region), feature extraction (micro textures) and classification using Linear Vector Quantizer-Artificial Neural Network (LVQ-ANN). The search area for glaucoma is the OD region wherein the cupping occurs, so in the first step ROI is extracted from the whole image. Feature extraction and classification are the most challenging tasks as the performance of the system depend both of them. Laws defined five spatial filters to extract micro-statistical estimators such as Level, Edge, Spot, Wave, and Ripple. Fundus images in three databases; DRISHTI-GS1, ORIGA, and RIM-ONE are classified using LVQ-ANN classifier. Results indicate the strength of the LVQ-ANN classifier for glaucoma diagnosis with sensitivity of 95.71% (DRISHTI-GS1), 83.33% (ORIGA) and 94.87% (RIM-ONE).

Downloads

Download data is not yet available.

References

X. Chen, Y. Xu, D.W.K. Wong, T.Y. Wong, and J. Liu, "Glaucoma detection based on the deep convolutional neural network," 37th annual international conference of the IEEE engineering in medicine and biology society, 2015, pp. 715-718.

B. Al-Bander, W. Al-Nuaimy, M.A. Al-Taee, and Y. Zheng, "Automated glaucoma diagnosis using deep learning approach," 14th International Multi-Conference on Systems, Signals & Devices, 2017, pp. 207-210.

M.V. Boland and H.A. Quigley, "Risk factors and open-angle glaucoma: classification and application," Journal of glaucoma, Vol. 16, No. 4, 2007, pp. 406-418.

E.A. Osman, B.A.M. Alqarni, S.S.H. AlHasani, S.S.S. Al Harbi, P.W. Gikandi, and A. Mousa, " Compliance of glaucoma patients to ocular hypotensive medications among the Saudi population," Journal of ocular pharmacology and therapeutics, Vol. 32, No. 1, 2016, pp. 50-54.

A. Ghosh, A. Sarkar, AS Ashour, D. Balas-Timar, N. Dey and V.E. Balas, "Grid color moment features in glaucoma classification," International Journal of Advanced Computer Science and Applications, Vol. 6, No. 9, 2015, pp.99-107.

A. Li, J. Cheng, D.W.K. Wong and J. Liu, "Integrating holistic and local deep features for glaucoma classification," 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016, pp. 1328-1331.

J.J. Gómez-Valverde, A. Antón, G. Fatti, B. Liefers, A. Herranz, A. Santos, and M.J. Ledesma-Carbayo, "Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning," Biomedical optics express, Vol. 10, No. 2, 2019, pp. 892-913.

S. Samanta, S.S. Ahmed, MAMM Salem, S.S. Nath, N. Dey, and S.S. Chowdhury, "Haralick features based automated glaucoma classification using backpropagation neural network," In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications, 2015, pp. 351-358.

A. El-Rafei, T. Engelhorn, S. Wärntges, A. Dörfler, J. Hornegger, and G. Michelson, "Glaucoma classification based on visual pathway analysis using diffusion tensor imaging," Magnetic resonance imaging, Vol. 31, No. 7, 2013, pp. 1081-1091.

F. Fink, K. Worle, P. Gruber, A.M. Tome, J.M. Gorriz-Saez, C.G. Puntonet, and E.W. Lang, "ICA analysis of retina images for glaucoma classification," 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 4664-4667.

D. Yadav, M.P. Sarathi and M.K. Dutta, "Classification of glaucoma based on texture feature using neural networks," Seventh International Conference on Contemporary Computing, 2014, pp. 109-112.

L. Li, M. Xu, H. Liu, Y. Li, X. Wang, L. Jiang, and N. Wang, “A large-scale database and a CNN model for attention-based glaucoma detection," IEEE transactions on medical imaging, Vol. 39, No. 2, 2019, pp.413-424.

J. Sivaswamy, S.R. Krishnadas, G.D. Joshi, M. Jain, and AUS. Tabish, “Drishti-gs: Retinal image dataset for optic nerve head (onh) segmentation," IEEE 11th International Symposium on Biomedical Imaging, 2014, pp. 53–56.

Z. Zhang, F.S. Yin, J. Liu, W.K. Wong, N.M. Tan, B.H. Lee, J. Cheng, and T.Y. Wong, “Origa-light: An online retinal fundus image database for glaucoma analysis and research," Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010, pp. 3065–3068.

F. Fumero, S. Alayón, J.L. Sanchez, J. Sigut, and M. Gonzalez-Hernandez, “RIM-ONE: An open retinal image database for optic nerve evaluation," 24th International Symposium on Computer-Based Medical Systems, 2011; pp. 1–6.

K.I Laws, “Rapid texture identification," International Society for Optics and Photonics, Vol. 238, 1980, pp. 376-381.

T. Kohonen, J. Kangas, J. Laaksonen, and K. Torkkola, “LVQ PAK: A program package for the correct application of Learning Vector Quantization algorithms," International Joint Conference on Neural Networks, Vol. 92, 1992, pp. 725-730.
Published
2021-06-30
How to Cite
Mukil Alagirisamy. (2021). MICRO STATISTICAL DESCRIPTORS FOR GLAUCOMA DIAGNOSIS USING NEURAL NETWORKS. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 7(1), 1-10. https://doi.org/10.29284/ijasis.7.1.2021.1-10
Section
Articles