A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images
Radhika R. 1*, Rashima Mahajan 1
Journal of Angiotherapy 8(3) 1-8 https://doi.org/10.25163/angiotherapy.839504
Submitted: 09 January 2024 Revised: 29 February 2024 Published: 04 March 2024
Enhancing medical image contrast improves diagnosis accuracy. COA-SCSO optimizes parameters efficiently, ensuring clearer images and better patient care outcomes.
Abstract
Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in 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.
View Dimensions
View Altmetric
Save
Citation
View
Share