Medical Imaging and Image Processing: A Comprehensive Review

Medical imaging plays a central role in modern healthcare by enabling non-invasive visualization of anatomical structures and physiological processes. Medical image processing refers to the set of computational techniques used to enhance, analyze, and interpret medical images obtained from various imaging modalities such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET). The integration of advanced image processing algorithms with medical imaging systems has significantly improved diagnostic accuracy, treatment planning, and clinical decision-making. This review article provides a comprehensive overview of medical imaging systems and the fundamental principles of medical image processing. Key preprocessing techniques, segmentation methods, feature extraction approaches, and image classification strategies are discussed in detail. In addition, recent advances in artificial intelligence, particularly deep learning and convolutional neural networks, are highlighted due to their transformative impact on automated image analysis. The clinical applications of medical image processing in disease diagnosis, therapy monitoring, and image-guided interventions are also reviewed. Furthermore, current challenges such as data variability, noise, ethical considerations, and the need for explainable algorithms are addressed. Finally, future research directions focusing on hybrid imaging systems, personalized medicine, and intelligent decision-support systems are explored. Overall, this review emphasizes the critical role of medical image processing in enhancing imaging systems and shaping the future of biomedical engineering and clinical practice.

کلیدواژه ها:

Medical Imaging ، Image Processing ، Biomedical Engineering ، Artificial Intelligence ، Deep Learning