Optimizing Medical Imaging Quality: An In-Depth Examination of Preprocessing Methods for Brain MRIs

Neurodegenerative diseases arise from the gradual deterioration of neuronal structure or function and spans different levels of neuronal circuitry in the brain, ranging from molecular to systemic. This progression can lead to eventual cell death and lack of a known means to arrest the ongoing degeneration of neurons renders these diseases incurable. Magnetic Resonance Imaging (MRI) plays a crucial role in both diagnosing and categorizing such diseases. In recent years, substantial research efforts have been dedicated to developing Artificial Intelligence (AI)-based methods for the automated diagnosis of brain disease. However, the efficacy of AI algorithms in this context is contingent upon the quality of preprocessing applied to input images. This article comprehensively reviews various preprocessing techniques aimed at enhancing MRI image quality and extracting pertinent features for AI-driven disease classification. Techniques such as reorientation, registration, skull stripping, and slicing are examined in detail, exploring their impact on both image quality and classification performance. The article also emphasizes the standardized preprocessing pipelines that underscore the ongoing need to optimize methods, enhancing AI-based classification for accurate early diagnoses of brain diseases and facilitating effective treatment strategies.