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

Radhika R.; Rashima Mahajan

doi:10.25163/angiotherapy.839504

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

REVIEWS (Open Access)

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

Radhika R. ^1*, Rashima Mahajan ¹

+ Author Affiliations

Journal of Angiotherapy 8 (3) 1-11 https://doi.org/10.25163/angiotherapy.839504

Submitted: 09 January 2024 Revised: 29 February 2024 Published: 04 March 2024

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.

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