Classification of Glaucoma Image Using Supervised Classifiers and Segmentation of Blood Vessel
Keywords:
Glaucoma Detection, Support Vector Machine, Random Forest, Digital Retinal Image for Vessel ExtractionAbstract
In this work, a variety of image processing approaches that are employed in the evaluation of the Cup to Disc ratio on the pre-processed retinal fundus pictures are discussed. The suggested method is divided into five main phases, including data collecting, picture pre-processing, segmentation of blood vessels, feature extraction, and classification for glaucoma detection by removing the blood vessel. First, the input photos are gathered from the DRIVE, HRF, DRIONS-DB, and STARE datasets, which stand for Structured Analysis of the Retina. The acquired retinal images are then cleaned up by median filtering and contrast limiting adaptive histogram equalisation techniques. Hybrid features are used to extract the feature values in order to improve classification performance. In the experimental phase, the proposed system improved the retinal blood vessel segmentation and classification up to 2% in comparison with other existing methods. All the experiments are evaluated through various performance indices like accuracy, sensitivity, specificity, precision, recall.
Downloads
References
Acharya, UR, Dua, S, Du, X & Chua, CK. (2011), 'Automated diagnosis of glaucoma using texture and higher order spectra features', IEEE Transactions on information technology in biomedicine, vol. 15, no. 3, pp. 449-455.
Acharya, UR, Ng, E, Eugene, LWJ, Noronha, KP, Min, LC, Nayak, KP &Bhandary, SV. ( 2015), 'Decision support system for the glaucoma using Gabor transformation', Biomedical Signal Processing and Control, vol. 15, pp. 18-26.
Akram, MU & Khan, SA. (2013), 'Multilayeredthresholding-based blood vessel segmentation for screening of diabetic retinopathy', Engineering with computers, vol. 29, no. 2, pp. 165-173.
Akram, MU, Tariq, A, Khalid, S, Javed, MY, Abbas, S &Yasin, UU. (2015), 'Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques', Australasian physical & engineering sciences in medicine, vol. 38, no. 4, pp. 643-655.
Al-Bander, B, Al-Nuaimy, W, Al-Taee, MA &Zheng, Y. (2017), 'Automated glaucoma diagnosis using deep learning approach', in 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 207-210.
Balasubramanian, T, Krishnan, S, Mohanakrishnan, M, Rao, KR, Kumar, CV &Nirmala, K. (2016), 'HOG feature based SVM classification of glaucomatous fundus image with extraction of blood vessels', in 2016 IEEE Annual India Conference (INDICON), pp. 1-4.
Banister, K, Boachie, C, Bourne, R, Cook, J, Burr, JM, Ramsay, C, Garway-Heath, D, Gray, J, McMeekin, P & Hernández, R. (2016), 'Can automated imaging for optic disc and retinal nerve fiber layer analysis aid glaucoma detection?', Ophthalmology, vol. 123, no. 5, pp. 930-938.
Benzebouchi, N, Azizi, N &Bouziane, S. (2018), 'Glaucoma diagnosis using cooperative convolutional neural networks', International Journal of Advances in Electronics and Computer Science, vol. 5, no. 1, pp. 31-36.
Bhartiya, S, Ichhpujani, P & Gandhi, M. (2019), 'The Glaucoma Treatment Paradigm: An Overview', in Glaucoma Drainage Devices, Springer, pp. 1-6.
Bhowmik, D, Kumar, KS, Deb, L, Paswan, S &Dutta, A. (2012), 'Glaucoma-A Eye Disorder Its Causes, Risk Factor, Prevention and Medication', The Pharma Innovation, vol. 1, no. 1, Part A, p. 66.
Bock, R, Meier, J, Nyúl, LG, Hornegger, J & Michelson, G. (2010), 'Glaucoma risk index: automated glaucoma detection from color fundus images', Medical image analysis, vol. 14, no. 3, pp. 471-481.
Carrillo, J, Bautista, L, Villamizar, J, Rueda, J & Sanchez, M. (2019), 'Glaucoma detection using fundus images of the eye', in 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), pp. 1-4.
Chai, Y, Liu, H &Xu, J. (2018)., 'Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models', Knowledge-Based Systems, vol. 161, pp. 147-156.
Chaikitmongkol, V, Khunsongkiet, P, Patikulsila, D, Ratanasukon, M, Watanachai, N, Jumroendararasame, C, Mayerle, CB, Han, IC, Chen, CJ &Winaikosol, P. (2018), 'Color fundus photography, optical coherence tomography, and fluorescein angiography in diagnosing Polypoidal Choroidalvasculopathy', American Journal of Ophthalmology, vol. 192, pp. 77-83.
Chang, DS, Arora, KS, Boland, MV, Supakontanasan, W & Friedman, DS. (2013), 'Development and validation of an associative model for the detection of glaucoma using pupillography', American journal of ophthalmology, vol. 156, no. 6, pp. 1285-1296. e1282.
Chaudhuri, S, Chatterjee, S, Katz, N, Nelson, M &Goldbaum, M. (1989), 'Detection of blood vessels in retinal images using two-dimensional matched filters', IEEE Transactions on medical imaging, vol. 8, no. 3, pp. 263-269.
Chen, X, Xu, Y, Wong, DWK, Wong, TY & Liu, J. (2015), 'Glaucoma detection based on deep convolutional neural network', in 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 715-718.
Rajasekaran, A. S., Yuvaraj, D., & Maria, A. (2022, March). Secure Authentication Scheme for Medical care applications based on IoT. In 2022 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1-5). IEEE.
Santhi, R., & Yuvaraj, D. (2017). Content-Based Image Retrieval in Cloud Using Watermark Protocol and Searchable Encryption.
Yuvaraj, D. ., Alnuaimi, S. S. ., Rasheed, B. H. ., Sivaram, M. ., & Porkodi, V. . (2024). Ontology Based Semantic Enrichment for Improved Information Retrieval model. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 70–77.
Krishnan, S. N., Yuvaraj, D., & Mathusudhanan, S. M. S. (2019). Novel Feature Extraction Methods for Effective Texture Image And Data Classifications. Journal of Electrical Engineering, 19(2), 10-10.
D.Yuvaraj, and S. Hariharan, “Content-based image retrieval based on integrating region segmentation and colour histogram”, The International Arab Journal of Information Technology, 2(11), 12, 2016.
Ahamed, B. B., Yuvaraj, D., & Priya, S. S. (2019). Image Denoising With Linear and Non-linear Filters. In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 806-810). IEEE.
Geetha, K., Yuvaraj, D.,(2018).A Novel Shape Feature Extraction Technique for Content-Based Image Retrieval (CBIR) Systems, International Journal of advance Science and Technology,28(2),158-169.
Yuvaraj, D., Sivaram, M., Karthikeyan, B., & Abdulazeez, J. (2019). Shape, colour and texture-based CBIR system using the fuzzy logic classifier. CMC-Computers Materials & Continua, 59(3), 729-739.
Sundara Vadivel, P., Yuvaraj, D., Navaneetha Krishnan, S., & Mathusudhanan, S. R. (2019). An efficient CBIR system based on colour histogram, edge, and texture features. Concurrency and Computation: Practice and Experience, 31(12), e4994.
RajeshKumar, N., Yuvaraj, D., Manikandan, G., BalaKrishnan, R., Karthikeyan, B., Narasimhan, D., & Raajan, N. R. (2020). Secret Image Communication Scheme Based on Visual Cryptography and Tetrolet Tiling Patterns. CMC-Computers Materials & Continua, 65(2), 1283-1301.
D.Yuvaraj, Salar Faisal Noori, Subbiah Swaminathan, (2021) Multi-perspective scaling convolutional neural networks for high-resolution MRI brain image segmentation, Materials Today: Proceedings
Krishnan, R. B., Yuvaraj, D., Devi, P. S., Chooralil, V. S., Kumar, N. R., Karthikeyan, B., & Manikandan, G. (2023). An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images. Computer Systems Science & Engineering, 46(2).
Sambandam, P., Yuvaraj, D., Padmakumari, P., & Swaminathan, S. (2023). Deep attention based optimized Bi-LSTM for improving geospatial data ontology. Data & Knowledge Engineering, 144, 102123.
Yuvaraj, D., Sampath, A. K., Shanmugapriya, N., Samatha, B., Arun, S., & Thiyagarajan, R. (2022, October). Medical Image Segmentation of Bio-medical Images with Deep Convolution Neural Networks using Ensemble Approach. In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1041-1049). IEEE.
Yuvaraj, D., Dinesh, M., Sivaram, M., & Nageswari, S. (2022). An efficient data mining process on temporal data using relevance feedback method. World Review of Science, Technology and Sustainable Development, 18(1), 20-30
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
