Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition—application to 3D brain imaging
In Artificial Intelligence
Abstract
We present a novel approach of model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs simultaneously segmentation and recognition of the scene and the solution of the recognition task is progressive, processing successively different objects, using different of knowledge about the object and about relationships between objects. Therefore the core of the approach is the representation part, and constitutes the main contribution of this paper. We make use of a spatial representation of each piece of information, as a spatial set representing a constraint to be satisfied by the searched object, thanks in particular to fuzzy mathematical operations. Fusion of these constraints allows to, segment and recognize the desired object.