Unsupervised Brain Tissue Segmentation with GMM and EM

Implemented an unsupervised pipeline for brain tissue segmentation using Gaussian Mixture Models (GMM) and Expectation-Maximization (EM). The pipeline utilizes k-means clustering for initialization and refines segmentation into Grey Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). The method was evaluated using Dice similarity scores, highlighting challenges and improvements across T1 and T2_FLAIR MRI images.

Keywords: Brain Tissue Segmentation, MRI, Gaussian Mixture Models, Expectation-Maximization, Dice Similarity, Medical Imaging, Grey Matter, White Matter, CSF

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