Data fusion and stochastic optimization: application to esophagus outer wall detection on ultrasound images

We propose a detection method of esophagus outer wall from endosonographic sequences (composed of separate slices uniformly distributed), which minimizes the information alterations due to the cooperation of different models. The kernel of the proposed solution is based on the use of a stochastic optimization algorithm, fully adapted to our particular case: the goal is to find the optimal contour, which verifies regularity conditions and maximizes a given criteria. Such a method presents the advantage of taking into consideration the entire searched space and thus, avoiding local minimum optimization problems. Moreover, this approach cooperates with a data fusion based processing, which allows a priori knowledge integration with its own inaccuracy. Detection robustness is finally maintained by the use of control agents, which take advantage of adjacent slices. At this level, fuzzy fusion and evidence theory are confronted. All these components are integrated in a coherent and cooperative architecture. Results obtained on real images acquired are very encouraging