Using Belief Function Theory to Deal with Uncertainties and Imprecisions in Image Processing

Abstract

In imaging, physical phenomena and acquisition system often induce an alteration of the information. It results in the presence of noise and partial volume effect corresponding respectively to uncertainties and imprecisions. To cope with these different imperfections, we propose a method based on information fusion using Belief function theory. First, it takes advantage of neighborhood information and combination rules on mono-modal images in order to reduce uncertainties due to noise while considering imprecisions due to partial volume effect on disjunctions. Imprecisions are then reduced using information coming from multi-modal images. Results obtained on simulated images using various signal to noise ratio and medical images show its ability to segment multi-modal images having both noise and partial volume effect.KeywordsRecognition RatePartial Volume EffectInformation FusionSimulated ImageImprecise InformationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.