A Comparative Study of Noise Reduction Techniques for Blood Vessels Image

The accurate analysis and interpretation of blood vessel images are essential for diagnosing and monitoring various medical conditions. However, these images often suffer from the presence of noise, which can hinder proper visualization and lead to erroneous interpretations. In this paper, we present a comprehensive comparative study of noise reduction techniques for blood vessel images, by a literature survey. The study encompasses both traditional and new methods, evaluating their performance, benefits, and challenges. Traditional methods, such as Anisotropic Diffusion Filtering and Wavelet Transform, have proven effective in preserving blood vessel structures and retaining fine details. However, they require careful parameter selection and may be computationally intensive. On the other hand, new techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE), Non-Local Mean Filter (NLM), and deep learning-based approaches, offer promising advancements in noise reduction capabilities with reduced computational complexity. The choice between traditional and new methods depends on specific application requirements, noise characteristics, and available computational resources. Our findings highlight the need for further research in parameter tuning, computational efficiency optimization, and hybrid approaches to enhance the noise reduction process in blood vessel images. This study contributes to the advancement of medical imaging by providing valuable insights for researchers and practitioners, enabling improved diagnostic accuracy and patient care.