An Improved Method for Cholesterol Detection Using Iris Analysis

Authors

  • Poovayar Priya M, Ezhilarasan M

Keywords:

Cholesterol, Modified Daugman method, Iridology, Iris segmentation, Iris enhancement, Iris normalization, Iris, heart disease.

Abstract

The presence of cholesterol in the iris can be detected in the cornea area which is a whitish ring-shape. It is a sign of hyperlipidemia and is also correlated with coronary heart disease (CHD). Iridology is an alternative method to detect the presence of cholesterol in the iris. We proposed a novel method of detecting the presence of cholesterol using image processing techniques. In this method, the iris is segmented from the eye, enhancing of iris, then normalization of the iris, and then cholesterol detection using the Modified Daugman method was implemented. This method involves 3 stages of cholesterol detection. The result showed an impressive result in the detection of the presence of cholesterol when applying the iris code of different bits.

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References

Sharan, F., 1989. Iridology: A complete guide to diagnosing through the iris and to related forms of treatment. HarperThorsons.

Jensen B," Iridology simplified", California: Bernard Jensen, 1980.

Gu, R., Wang, G., Song, T., Huang, R., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T. and Zhang, S., 2020. CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE transactions on medical imaging, 40(2), pp.699-711.

Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A. and Jégou, H., 2021, July. Training data-efficient image transformers & distillation through attention. In International conference on machine learning (pp. 10347-10357). PMLR.

Wang, Y., Seo, J. and Jeon, T., 2021. NL-LinkNet: Toward lighter but more accurate road extraction with nonlocal operations. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.

Chen, Z., Zeng, H., Yang, W. and Chen, J., 2022, November. Texture Enhancement Method of Oceanic Internal Waves in SAR Images Based on Non-local Mean Filtering and Multi-scale Retinex. In 2022 3rd China International SAR Symposium (CISS) (pp. 1-5). IEEE.

S. Agrawal, R. Panda, P. K. Mishro, and A. Abraham, ‘‘A novel joint histogram equalization based image contrast enhancement,’’ J. King Saud Univ.-Comput. Inf. Sci., vol. 34, no. 4, pp. 1172–1182, Apr. 2022, doi: 10.1016/j.jksuci.2019.05.010.

B. S. Rao, ‘‘Dynamic histogram equalization for contrast enhancement for digital images,’’ Appl. Soft Comput., vol. 89, Apr. 2020, Art. no. 106114, doi: 10.1016/j.asoc.2020.106114.

S. Doshvarpassand, X. Wang, and X. Zhao, ‘‘Sub-surface metal loss defect detection using cold thermography and dynamic reference reconstruction (DRR),’’ Struct. Health Monitor., vol. 21, no. 2, pp. 354–369, Mar. 2022, doi: 10.1177/1475921721999599.

J. Murugachandravel and S. Anand, ‘‘Enhancing MRI brain images using contourlet transform and adaptive histogram equalization,’’ J. Med. Imag. Health Informat., vol. 11, no. 12, pp. 3024–3027, Dec. 2021, doi: 10.1166/jmihi.2021.3906.

S. F. M. Radzi, M. K. A. Karim, M. I. Saripan, M. A. A. Rahman, N. H. Osman, E. Z. Dalah, and N. M. Noor, ‘‘Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph,’’ IEEE Access, vol. 8, pp. 127720–127731, 2020, doi: 10.1109/ACCESS.2020.3008927.

U. Kuran and E. C. Kuran, ‘‘Parameter selection for CLAHE using multi-objective cuckoo search algorithm for image contrast enhancement,’’ Intell. Syst. with Appl., vol. 12, Nov. 2021, Art. no. 200051, doi: 10.1016/j.iswa.2021.200051.

M. R. Islam and M. Nahiduzzaman, ‘‘Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach,’’ Exp. Syst. Appl., vol. 195, Jun. 2022, Art. no. 116554, doi: 10.1016/j.eswa.2022.116554.

Y. Yang, Z. Jiang, C. Yang, Z. Xia, and F. Liu, “Improved retinex image enhancement algorithm based on bilateral filtering,” in Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015.

Priyal, M.P. and Ezhilarasan, M., 2023, November. IRIS Segmentation Technique Using IRIS-UNet Method. In Advanced Concepts for Intelligent Vision Systems: 21st International Conference, ACIVS 2023 Kumamoto, Japan, August 21–23, 2023 Proceedings (Vol. 14124, p. 235). Springer Nature.

Daugman, J., 2007. New methods in iris recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(5), pp.1167-1175.

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Published

24.03.2024

How to Cite

Poovayar Priya M. (2024). An Improved Method for Cholesterol Detection Using Iris Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 3827–3832. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6066

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Section

Research Article