MRI-Based Cellular Disease Detection: A Methodological Approach Using Morphological Processing and Fuzzy Clustering

The detection of cellular diseases from MRI scans is critical for early diagnosis and effective treatment. This paper presents a robust methodology integrating morphological operations, skull stripping, and fuzzy c-means clustering to preprocess and analyze brain MRI images. The proposed approach enhances image quality, isolates the brain region, and segments disease-affected areas with high precision. By leveraging these advanced techniques, the study achieves improved accuracy in detecting cellular abnormalities, offering a reliable framework for medical diagnostics. Experimental results demonstrate a classification accuracy of 95%, underscoring the potential of this methodology in clinical applications.