Possibilistic registration based on unsupervised classification (BMPRUC)

In this paper, an unsupervised registration approach based on possibility theory, called "Unsupervised Possibilistic registration", is proposed to encounter this problem. It consists on adding an unsupervised projection step that allows matching possibility maps, obtained from the two images instead of the grey-level images (knowing that the thematic classes and their number have no effect on the registration). The experiments and the comparative study using MRI images have shown promising results. It is shown that the proposed unsupervised registration approach overcomes major problems of existing methods and allows temporal complexity optimization.