Evidential Deformable Model for Contour Tracking. Application on Brain Cine MR Sequences

Abstract

The goal of this paper is to introduce an efficient evidential particle filter for complex shapes tracking. The particularity of that particle filter is not only the fair use of the observation at the current time in the update step of it by performing a curve evolution but also it represents a bridge between Probability function and Evidence theory. This bridge can be illustrated by incorporating a data fusion step in the update phase. This method builds a track by selecting the best particles between the particle candidates. This re-sampling phase is based on choosing the particles possessing the higher value of the basic belief assignment function. The values of these basic belief assignment functions are resulting from the fusion process of two distinctive sources of information. The first source is the energy functional and the second one is the local sensitive histogram. The evaluation of our approach, which is made on a realistic Brain cine RM sequences, aims at tracking the motion of the walls of the third ventricle. Therefore, the latter shows its obvious and clear efficiency. In order to validate our proposal, we present a comparative study between our proposal and the state of the art methods. The obtained results are encouraging.