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Digital Image Processing-CAP 5400 MRI Segmentation Using Fuzzy C-Means and Finite Gaussian Mixture Model

MRI Segmentation assumes great importance research and clinical applications. There are many methods that exist to segment the brain. Of these, conventional methods that use pure image processing techniques are not preferred because they need human interaction for accurate and reliable segmentation. Unsupervised methods, on the other hand, do not require any human interference and can segment the brain with high precision. For this reason, unsupervised methods are preferred over conventional methods. Many unsupervised methods such as Fuzzy c-means, Finite Gaussian Mixture Model, Artificial Neural Networks, etc. exist. In this project, we consider two such unsupervised methods based on FCM and FGMM. Results show that FCM performs better in terms of computational complexity and accuracy, while FGMM has advantages such as learning the number of classes automatically. We observed that FGMM is very sensitive to external parameters d0 and threshold T and an accurate method to determine these parameters based on the image being segmented yields better results.