Brain Tumor Detection in MRI Images Using Classical Image Processing Techniques

This research introduces a stepwise method for enhancing and segmenting MRI brain scans, utilizing classical image processing techniques including anisotropic diffusion, skull extraction, and contrast enhancement strategies like top-hat and histogram-based filtering. The tumor segmentation process leverages the watershed transform for precise boundary detection. The entire workflow, developed in Python with OpenCV and scikit-image, was applied to a curated set of 80 brain MRI samples. Qualitative analysis suggests that this resource-efficient and explainable pipeline reliably identifies tumor regions with high clarity — making it practical for clinical environments with limited computational infrastructure.