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Particle Swarm Optimization Based Retinal Image Enhancement

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Abstract

Rear portion of the eye can be pictured by the retinal fundus imaging technique. The retinal fundus images provide clear description about the retinal blood vessels and optic disc regions. Most of the retinal diseases have perceptible symptoms on these regions and hence this can facilitate the optometrist for early diagnosis and treatment of retinal diseases. Blur, uneven illumination and poor contrast in the retinal fundus images are the commonly seen challenges for both manual and automated image analyzing systems. In this paper, a new method is proposed for the enhancement of uneven illuminance and contrast fundus images by adjusting their luminance and contrast based on Particle swarm optimization (PSO). The proposed method incorporating gamma correction on the HSV color space and contrast adjustment on the LAB color space. The contrast adjustment is optimized by the PSO algorithm to improve the enhancement process. The proposed method is evaluated on the publicly available databases DIARET DB0 and DIARET DB1. The Structural Similarity Index and Peak Signal to Noise Ratio are the performance measures used to evaluate the enhancement method. From the results obtained it is inferred that the fundus image with uneven illumination and contrast is enhanced in a better way by the proposed method.

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References

  1. Singh, N., Kaur, L., & Singh, K. (2019). Histogram equalization techniques for enhancement of low radiance retinal images for early detection of diabetic retinopathy. Engineering Science and Technology, an International Journal, 22 (3), 736–745.

    Article  MathSciNet  Google Scholar 

  2. Pradeep, A., Joseph, X. F., & Sreeja, K. A. (2021). Retinal image enhancement by intensity index based histogram equalization for diabetic retinopathy screening. In 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. Springer.

  3. Roman, J. C. M., Noguera, J. L. V., García-Torres, M., Benítez, V. E. C., & Matto, I. C. (2021). Retinal Image Enhancement via a Multiscale Morphological Approach with OCCO Filter, ICITS 2021. Advances in Intelligent Systems and Computing, vol. 1330. Springer, Cham. https://doi.org/10.1007/978-3-030-68285-9:18

  4. Tiwari, M., Ruhela, R., & Gupta, B. (2020). An approach for color retinal image enhancement using linearly quantile separated histogram equalization and DCT based local contrast enhancement, advances in signal processing and intelligent recognition systems (Vol. 1209). Singapore: Springer.

    Google Scholar 

  5. Xiong, L., Li, H., & Xu, L. (2017). An enhancement method for color retinal images based on image formation model, computer methods and programs in biomedicine, Clerk Maxwell, a treatise on electricity and magnetism (pp. 68–73). Oxford: Clarendon, 1892.

    Google Scholar 

  6. Zhou, M., Jin, K., Wang, S., Ye, J., & Qian, D. (2017). Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Transactions on Biomedical engineering, 65 (3), 521–527.

    Article  Google Scholar 

  7. Liu, C. C., Chang, M. J., Chaung, Y. J., & Yu, S. S. (2016). A novel retinal image color texture enhancement method based on multi-regression analysis. In International symposium on computer, consumer and control.

  8. Bandara, A. M. R. R., & Giragama, P. W. G. R. M. P. B. (2017). A retinal image enhancement technique for blood vessel segmentation algorithm. In 2017 IEEE international conference on industrial and information systems (ICIIS).

  9. Sahu, S., Singh, A. K., Ghrera, S. P., & Elhoseny, M. (2019). An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Optics and Laser Technology, 110, 87–98.

    Article  Google Scholar 

  10. Veluchamy, M., & Subramani, B. (2019). Image contrast and color enhancement using adaptive gamma correction and histogram equalization. Optik, 183, 329–337.

    Article  Google Scholar 

  11. Zarie, M., Hajghassem, H., & Majd, A. E. (2018). Contrast enhancement using triple dynamic clipped histogram equalization based on mean or median. Optik, 175, 126–137.

    Article  Google Scholar 

  12. Agarwal, M., & Mahajan, R. (2018). Medical image contrast enhancement using range limited weighted histogram equalization. Procedia Computer Science, 125, 149–156.

    Article  Google Scholar 

  13. Singh, H., Kumar, A., Balyan, L. K., & Singh, G. K. (2018). Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement. Computers and Electrical Engineering, 70, 462–475.

    Article  Google Scholar 

  14. AmandeepKaur, ChandanSingh. (2017). Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Applied Soft Computing, 51, 180–191.

    Article  Google Scholar 

  15. ZhuRong, Zhu Li. (2015). Dong-nan, study of color heritage image enhancement algorithms based on histogram equalization. Optik, 126 (24), 5665–5667.

    Article  Google Scholar 

  16. Dai, P., Sheng, H., Zhang, J., Li, L., Wu, J., & Fan, M. (2016). Retinal fundus image enhancement using the normalized convolution and noise removing. International Journal of Biomedical Imaging, 5075612, 1–12.

    Article  Google Scholar 

  17. Zhe, L. I., Ruilian, T. A. N., & Baoxiang, R. E. N. (2016). Research on particle swarm optimization of variable parameter. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

  18. Naik, S. K., & Murthy, C. A. (2003). Hue-preserving color image enhancement without gamut problem. IEEE Transactions on image processing, 12 (12), 1591–1598.

    Article  Google Scholar 

  19. Li, X., Min, W., & Yue, W. (2013). Moving object detection using LAB color space. Journal of Huazhong University of Science and Technology (Natural Science Edition)., 41 (1), 219–222.

    Google Scholar 

  20. Gupta, B., & Tiwari, M. (2019). Color retinal image enhancement using luminosity and quantile based contrast enhancement. Multidimensional Systems and Signal Processing, 30, 1829–1837. https://doi.org/10.1007/s11045-019-00630-1.

    Article  MATH  Google Scholar 

  21. Chen, Y. C., Wang, H. C., & Su, T. J. (2006). Particle swarm optimization for image noise cancellation. In 1st International Conference on Innovative Computing, Information and Control-Volume I (ICICIC’06) (Vol. 1, pp. 587–590).

  22. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks (Vol. 4, pp. 1942–1948). https://doi.org/10.1109/ICNN.1995.488968.

  23. Richer, T. J., & Blackwell, T. M. (2006). When is a swarm necessary?. In 2006 IEEE International Conference on Evolutionary Computation (CEC), Vancouver (pp. 1469–1476). BC: Canada.

  24. Lin, J. H., & Cheng, T. Y. (2005). Dynamic clustering using support vector learning with particle swarm optimization. In 18th international conference on systems engineering (pp. 218–223).

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Authors and Affiliations

  1. Mepco Schlenk Engineering College, Sivakasi, India

    V. Sathananthavathi & G. Indumathi

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  1. V. Sathananthavathi
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  2. G. Indumathi
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Correspondence to V. Sathananthavathi.

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Sathananthavathi, V., Indumathi, G. Particle Swarm Optimization Based Retinal Image Enhancement. Wireless Pers Commun 121, 543–555 (2021). https://doi.org/10.1007/s11277-021-08649-z

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