Evidential reasoning based on interval-based Bayesian belief networks

We have developed interval-based Bayesian belief networks and used them in a medical image recognition system to identify brain anatomy in a set of multi-modality images. Head images from the multi-modalities of x-ray CT, proton density MR, and T2-weighted MR are used in the proposed medical image recognition system. To incorporate the capability of handling uncertainty in the medical image recognition system, we use interval-based probability to represent incomplete information and uncertain evidence and the interval-based Bayesian belief networks to provide the framework for evidence accumulation and spatial reasoning. In evidence accumulation, the regional information extracted from the CT and MR images is converted to evidence in the form of probability intervals and organized in a hierarchical structure. The evidence is integrated by the belief networks. In spatial reasoning, the knowledge of spatial relationships among objects is organized in hierarchical structure with pairwise spatial relationships. The interval-based Bayesian belief networks and a prediction box are combined to hypothesize the potential location and likelihood of the target object. In the proposed medical image system, the use of the interval-based Bayesian belief networks provides flexibility for representing and handling uncertain evidence and reduces the distraction in defining point probabilities for the domain knowledge. It also allows the proposed recognition system to perform reasoning efficiently and identify brain anatomy effectively.