Magnetic Resonance Imaging (MRI) plays a pivotal role in non-invasive medical diagnostics and treatment planning particularly in the field of neuroimaging. One essential aspect of brain Magnetic Resonance (MR) image analysis is edge detection, a fundamental image processing task that aids in the identification of anatomical structures and abnormalities. This research paper presents a comprehensive comparative study of various edge detection methods applied to brain MR images, with the aim of evaluating their performance and applicability in clinical settings. Several well-established edge detection algorithms are implemented and fine-tuned for the specific characteristics of MRI data both in qualitative and quantitative way in the context of improving medical image segmentation, disease detection, and treatment planning. The quantitative assessment has been evaluated in terms of full reference, human visual and no reference image assessment parameter metrics. Center for Biomedical Research Excellence (COBRE) functional MR image datasets are used for this study. The results reveal valuable insights into the strengths and weaknesses of different edge detection methods when applied to MRI images.