Image analysis using iconic fuzzy sets

A formalism is presented for the iconic representation of knowledge in a semantic net. Nodes are represented as fuzzy sets, relations as functions over fuzzy sets. Moreover, the author demonstrates a consistent framework for the transition from the symbolic to the iconic level. He also defines a procedure for the explication, fuzzyfication and operationalization of natural language description predicates. This concept has successfully been used for a model-driven image analysis approach in the domain of MRI (magnetic resonance imaging) brain slices. The concept also has the potential for a data-driven approach. Measurements of features of fuzzy image structures can be represented in the semantic net as fuzzy assertions. Possible faults (pathologies) can be incorporated by using information form other sources (neurological studies) as (fuzzy) assertions. >