A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Radhika R. ^1*, Rashima Mahajan ¹

Enhancing medical image contrast improves diagnosis accuracy. COA-SCSO optimizes parameters efficiently, ensuring clearer images and better patient care outcomes.

Abstract

Medical image processing is vital for diagnosing and treating diseases, notably using Magnetic Resonance Imaging (MRI). A prior study introduced an algorithm for MRI image noise reduction, preserving key structures while suppressing mixed noise, though facing challenges in parameter optimization and occasional edge preservation failures. This study proposes a solution, the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO), to enhance MRI image contrast, addressing contrast enhancement limitations in medical image processing. Analyzing cardiac MRI images from SCMR consensus and AMRG Atlas databases, spatial information is emphasized for enhancement. The method integrates a cost function with a contrast measure based on multiple metrics, employing soft-computing approaches to reduce time complexity and expand the search pattern through a spatial domain transformation function. Performance evaluation, incorporating parameters like PSNR, MSE, NAE, and SSIM, shows the proposed algorithm achieving high PSNR (98) and SSIM (0.99) values, and low NAE (-0.17) and MSE (0.16) values compared to existing methods. The COA-SCSO approach demonstrates promising results in MRI image contrast enhancement, with potential implications for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validate effectiveness on larger datasets for clinical practice.

Keywords: Magnetic Resonance Image, Contrast enhancement, Cetacean optimization algorithm, Sand Cat Swarm Optimization algorithm.

References

Acharya, U. K., & Kumar, S. (2020). Particle swarm optimized texture based histogram equalization (PSOTHE) for MRI brain image enhancement. Optik, 224, 165760.

Chen, J., Yu, W., Tian, J., Chen, L., & Zhou, Z. (2018). Image contrast enhancement using an artificial bee colony algorithm. Swarm and Evolutionary Computation, 38, 287-294.

Cooper, L. A., Carter, A. B., Farris, A. B., Wang, F., Kong, J., Gutman, D. A., ... & Saltz, J. H. (2012). Digital pathology: Data-intensive frontier in medical imaging. Proceedings of the IEEE, 100(4), 991-1003.

Draa, A., & Bouaziz, A. (2014). An artificial bee colony algorithm for image contrast enhancement. Swarm and Evolutionary computation, 16, 69-84.

Gharehchopogh, F. S., & Gholizadeh, H. (2019). A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 48, 1-24.

Gorai, A., & Ghosh, A. (2011, September). Hue-preserving color image enhancement using particle swarm optimization. In 2011 IEEE Recent Advances in Intelligent Computational Systems (pp. 563-568). IEEE.

Islam, S. M., & Mondal, H. S. (2019, July). Image enhancement based medical image analysis. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.

Kandhway, P., & Bhandari, A. K. (2019). An optimal adaptive thresholding based sub-histogram equalization for brightness preserving image contrast enhancement. Multidimensional systems and signal processing, 30, 1859-1894.

Khanna, K., & Madan Arora, S. (2016). Ant colony optimization towards image processing. Indian J Sci Technol, 9(48), 1-9.

Lozano-Vázquez, L. V., Miura, J., Rosales-Silva, A. J., Luviano-Juárez, A., & Mújica-Vargas, D. (2022). Analysis of Different Image Enhancement and Feature Extraction Methods. Mathematics, 10(14), 2407.

Luo, W., Duan, S., & Zheng, J. (2021). Underwater image restoration and enhancement based on a fusion algorithm with color balance, contrast optimization, and histogram stretching. IEEE Access, 9, 31792-31804.

Luque-Chang, A., Cuevas, E., Pérez-Cisneros, M., Fausto, F., Valdivia-Gonzalez, A., & Sarkar, R. (2021). Moth swarm algorithm for image contrast enhancement. Knowledge-Based Systems, 212, 106607.

Ma, J. J., Nakarmi, U., Kin, C. Y. S., Sandino, C. M., Cheng, J. Y., Syed, A. B., ... & Vasanawala, S. S. (2020, April). Diagnostic image quality assessment and classification in medical imaging: Opportunities and challenges. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 337-340). IEEE.

Monika Agarwal, Rashima Mahajan (2017) “Medical Images Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering” Procedia Computer Science, 115:509-517.

Monika Agarwal, Rashima Mahajan (2018) “Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization” Procedia Computer Science, 125:149-156.

Muniyappan, S., & Rajendran, P. (2019). Contrast enhancement of medical images through adaptive genetic algorithm (AGA) over genetic algorithm (GA) and particle swarm optimization (PSO). Multimedia Tools and Applications, 78, 6487-6511.

Navaneetha Krishnan, S., Yuvaraj, D., Banerjee, K., Josephson, P. J., Kumar, T., & Ayoobkhan, M. U. A. (2022). Medical image enhancement in health care applications using modified sun flower optimization. Optik, 271, 170051.

Qi, Y., Yang, Z., Sun, W., Lou, M., Lian, J., Zhao, W., ... & Ma, Y. (2021). A comprehensive overview of image enhancement techniques. Archives of Computational Methods in Engineering, 1-25.

Rundo, L., Tangherloni, A., Nobile, M. S., Militello, C., Besozzi, D., Mauri, G., & Cazzaniga, P. (2019). MedGA: a novel evolutionary method for image enhancement in medical imaging systems. Expert Systems with Applications, 119, 387-399.

Rundo, L., Tangherloni, A., Nobile, M. S., Militello, C., Besozzi, D., Mauri, G., & Cazzaniga, P. (2019). MedGA: a novel evolutionary method for image enhancement in medical imaging systems. Expert Systems with Applications, 119, 387-399.

Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25.

Singh, M., Sharma, N., Verma, A., & Sharma, S. (2016). Dynamic stochastic resonance based diffusion-weighted magnetic resonance image enhancement using multi-objective particle swarm optimization. Journal of Medical and Biological Engineering, 36, 891-900.

Sprawls, P. (2014). Optimizing medical image contrast, detail and noise in the digital era. Medical Physics International, 2(1).

Suetens, P. (2017). Fundamentals of medical imaging. Cambridge university press.

Tzalavra, A. G., Andreadis, I., V Dalakleidi, K., Constantinidis, F., I Zacharaki, E., & S Nikita, K. (2022). Dynamic contrast enhanced-magnetic resonance imaging radiomics combined with a hybrid adaptive neuro-fuzzy inference system-particle swarm optimization approach for breast tumour classification. Expert Systems, 39(4), e12895.

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

Verma, O. P., Chopra, R. R., & Gupta, A. (2016, March). An adaptive bacterial foraging algorithm for color image enhancement. In 2016 Annual Conference on Information Science and Systems (CISS) (pp. 1-6). IEEE.

Wadhwa, A., & Bhardwaj, A. (2021). Contrast enhancement of MRI images using morphological transforms and PSO. Multimedia Tools and Applications, 80, 21595-21613.

Zhou, Y., Shi, C., Lai, B., & Jimenez, G. (2019). CE of medical images using a new version of the world cup optimization algorithm. Quantitative imaging in medicine and surgery, 9(9), 1528.