Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods

Open Access

Year : 2023 | Volume :8 | Issue : 3 | Page : 11-18
By

    Snehalatha Katha

  1. Mohan Das Talari

  1. Assistant Professor, Jawaharlal Nehru Technological University Hyderabad University College of Engineering Sultanpur, Telangana, India
  2. Assistant Professor, Jawaharlal Nehru Technological University Hyderabad University College of Engineering Sultanpur, Telangana, India

Abstract

In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it’s difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc’s boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc’s colour and texture, so an automated retinal image analysis system must identify and remove these areas. DR is diagnosed early in this study using machine learning (ML) approaches. For example: Bayesian Classification; K-Means Clustering; PNN; SVM; and Bayesian Classification In order to determine the most effective strategy, these options will be weighed against one another and evaluated. For training and testing, a total of 300 fundus images are processed. Using image processing techniques, these raw photos are processed to extract the features. The results of an experiment show that PNN, Bayes Classifications, SVM, and K-Means Clustering are all more accurate than 94% of the time. It appears that SVM is the best method for detecting early signs of degenerative disease.

Keywords: Segmentation, edge detection, diabetic retinipathy, image enhancement, eye fundus

[This article belongs to Recent Trends in Sensor Research & Technology(rtsrt)]

How to cite this article: Snehalatha Katha, Mohan Das Talari , Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods rtsrt 2023; 8:11-18
How to cite this URL: Snehalatha Katha, Mohan Das Talari , Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods rtsrt 2023 {cited 2023 Jan 25};8:11-18. Available from: https://journals.stmjournals.com/rtsrt/article=2023/view=90571

Full Text PDF Download

Browse Figures

References

1. Parashuram Bannigidad and Asmita Deshpande, “A Hybrid Approach for Digital Fundus Images using Image Enhancement Techniques”, International Journal of Computer Engineering and Applications, Vol. XII, Issue I, 2017, pp.122-131.
2. Parashuram Bannigidad and Asmita Deshpande, “A Multistage Approach for exudates detection in fundus images using texture features with k-NN classifier”, International Journal of Advanced Research in Computer Science, Vol. 9, No. 1, 2018, pp.1-5.
3. Parashuram Bannigidad and Asmita Deshpande, “Exudates Detection in Digital Fundus Images using GLCM features with SVM classifier”, International Journal of Modern Electronic and Communication Engineering, Vol. 6, Issue. 6, 2018, pp.184-189.
4. B. Dorizzi, G. Tozatto, R. Varej, E. Ottoni, and T. Salles, ‘‘Diabetic retinopathy detection using red lesion localization and convolutional neural networks ~ o Andre a,” vol. 116, no. November 2019, 2020, 10.1016/ j. compbiomed.2019.103537.
5. Sakshi Gunde, A.A., Gupta, S.D., 2020. Diabetic retinopathy detection using nonmydriatic fundus images. Our Herit 1, 141–145.
6. Leeza, M., Farooq, H., 2019. Detection of severity level of diabetic retinopathy using Bag of features model. IET Comput. Vis. 13 (5), 523–530. https://doi.org/ 10.1049/cvi2.v13.510.1049/iet-cvi.2018.5263.
7. S. S. Rahim, V. Palade, C. Jayne, A. Holzinger, and J. Shuttleworth, ‘‘Detection of Diabetic Retinopathy and Maculopathy in EYe Fundus images using Fundus image processing,” vol. 9250, pp. 275–284, 2015, 10.1007/978-3-319-23344-4.
8. Soomro, T.A., Gao, J., Khan, T., Hani, A.F.M., Khan, M.A.U., Paul, M., 2017. Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: A survey. Pattern Anal. Appl. 20 (4), 927–961. https:// doi.org/10.1007/s10044-017-0630-y.
9. J. Hemanth and J. Anitha, “”Hybrid clustering method for optic disc segmentation and feature extraction in retinal images””, World Congress on Information and Communication Technologies, Trivandrum, 2012, pp. 320-325.
10. Juan Xua, Opas Chutatapeb, Eric Sungc, Ce Zhengd, Paul ChewTec Kuand, “Optic disk feature extraction via modified deformable model technique for glaucoma analysis”, Pattern Recognition, Vol. 40, Issue 7, 2007, pp. 2063-2076.
11. Arturo Aquino, Manuel Emilio Gegúndez-Arias, and Diego Marí, “Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques”, IEEE transactions on medical imaging, Volume. 29, no. 11, 2010, pp. 1860-1869.
12. Muhammad Abdullah, Muhammad Moazam Fraz, and Sarah A. Barman, “Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm”, PeerJ4: e2003; DOI10.7717/peerj.2003.
13. J. Sivaswamy, S. R. Krishnadas, G. Datt Joshi, M. Jain and A. U. Syed Tabish, “”Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation””, 2014, IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, 2014, pp. 53-56.
14. Image Database. (2007). DIARETDB1-Standard Diabetic Retinopathy Database Calibration level 1 [online]. Available from http://www.it.lut.fi/project/imageret/diaretdb1/
15. Wikipedia. (2022). Sensitivity and specificity. [online]. Available from https://en.wikipedia.org/wiki/Sensitivity_and_specificity.


Regular Issue Open Access Article
Volume 8
Issue 3
Received February 15, 2022
Accepted February 25, 2022
Published January 25, 2023