Image Processing in MRI: A Methodology Review

Abstract

Magnetic Resonance Imaging (MRI) plays a crucial role in medical diagnostics in modern times, allowing a non-invasive way to peer into and measure the intricately woven structures within the human body. Magnetic resonance imaging(MRI) and computed tomography (CT) are among the most used medical imaging modalities and give great insights into the internal organs and the tissues. Brain imaging has been particularly impressive in providing tools to aid in detection, diagnosis, and monitoring of conditions like tumors. However, the brain being highly complex in anatomy with high presence of noise and artifacts in medical images poses difficulty in precise diagnosis that offers detailed and high-resolution images of internal body structure. In this paper, an effort was put to review the techniques in MRI image processing. Radiology image processing improves diagnostic information and is a critical step in image processing that can make an image suitable for diagnostic preprocessing methods, such as noise reduction to enhance image quality making it more accurate for segmentation. Segmentation which isolates the areas of interest. Feature extraction to reduce dimensionality of data, various morphological operations and classification methods to reduce time of diagnosis and improving consistency in healthcare systems. To integrate advances in preprocessing, segmentation, and classification, this paper highlights the transformational impact of these methodologies on enhancing diagnostic accuracy, efficiency, and reliability in tumor identification.