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Background: Angiography is a medical imaging technique that uses X-ray imaging to visualize blood vessels in the body, aiding in the diagnosis of vascular conditions. Coronary angiography is a vital medical procedure that provides detailed images of a patient's coronary arteries and helps diagnose heart-related conditions. Challenges in coronary angiography involve image noise, which can reduce image quality and make it difficult to identify vascular structures. Additionally, variations in contrast and the presence of artifacts can impact the accuracy of diagnoses. To improve the image quality and enhance the image visibility needed to address the above challenges, this article proposed a novel hybrid approach called the Mean Total Variation Filter (MTVF). Materials & Methods: The study used an experimental design to assess how well the Mean Total Variation Filter (MTVF) improves the quality of coronary angiography images. The medical images were obtained in a controlled lab setting, where the research was carried out and the subsequent analysis was performed. The study spanned approximately six months, covering the phases of developing the algorithm, acquiring images, and evaluating performance. To select images, a purposive sampling method was employed, focusing on coronary angiography images with various levels of noise and artifacts. This method ensures that the selected images represent common challenges found in clinical practice. Preliminary studies suggested a minimum of 30 images were needed to achieve statistically significant results, ensuring enough test cases to accurately evaluate the proposed algorithm's performance. The effectiveness of the MTVF approach was measured using several quantitative metrics: Correlation Coefficient (CoC), Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Root Mean Square Error (RMSE). These metrics were chosen to thoroughly assess the algorithm's capability in reducing noise and enhancing image quality. Results: The performance of the proposed algorithm was assessed using various parameters for test image 1. The evaluation results indicate a high CoC of 0.9997, an impressive PSNR of 51.01, a low MSE of 1.3020, and a minimal RMSE of 0.0133. These metrics collectively highlight the algorithm's ability to produce excellent results in enhancing the quality of test image 1, making it a promising technique for noise reduction and image enhancement. Conclusion: The analysis highlights the outstanding performance of the proposed hybrid MTVF method in removing noise, outperforming current techniques. [ABSTRACT FROM AUTHOR] |
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