Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

Medical imaging plays an essential role in modern healthcare, helping accurate diagnoses and effective treatment strategies. Still, the quality and interpretability of medical images are regularly hindered by various sources of noise. This paper presents a comprehensive exploration of traditional noise reduction techniques in medical imaging, addressing challenges posed by quantum noise, electronic noise, radiation interference, and other factors. The study delves into spatial filtering, frequency domain filtering, statistical methods, probability-based noise reduction, and adaptive filtering techniques. Each method is analyzed for its applicability and effectiveness in mitigating noise while preserving diagnostically relevant information. The comparative analysis provides insights into the strengths and limitations of these techniques, guiding practitioners in selecting appropriate methods based on imaging modalities and noise characteristics. Also, the paper highlights future research directions, emphasizing the potential of advanced Machine Learning (ML) models and the integration of multimodal data for enhanced noise removal.