Brain MRI thresholding using incomparability and overlap functions

In this work we present a new image thresholding algorithm for the segmentation of MRI brain images into two classes: gray matter and white matter. The proposed algorithm is based on the concept of incomparability proposed by Fodor and Roubens for fuzzy preference relations. We test our algorithm for local and global segmentation of brain images. We proof that global segmentation performs better results than local segmentation and improves the results obtained by other thresholding algorithm.