Fuzzy fusion and belief updating: application to esophagus wall detection on ultrasound images

In medical ultrasound imaging, information is corrupted by inaccuracy (due to data, acquisition modality, noise), uncertainty (due to noise and missing data) and ambiguity (several anatomical structures having the same ultrasound respond). In this work, we propose a 3D segmentation method of esophagus inner and outer wall from endosonographic sequences (composed of separate slices uniformly distributed), which minimizes these information alterations thanks to the cooperation of different models. 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 surface, which verifies regularity conditions and maximizes a given criteria. Moreover, this approach cooperates with a data fusion based processing, which allows a prior knowledge integration with its own inaccuracy. All these components are integrated in a coherent architecture hierarchically organized which allows belief updating. First results obtained on real images acquired in a medical center are presented.