  One important challenge in modern Content-Based Medical Image Retrieval
(CBMIR) approaches is represented by the semantic gap, related to the
complexity of the medical knowledge. Among the methods that are able to close
this gap in CBMIR, the use of medical thesauri/ontologies has interesting
perspectives due to the possibility of accessing on-line updated relevant
webservices and to extract real-time medical semantic structured information.
The CBMIR approach proposed in this paper uses the Unified Medical Language
System's (UMLS) Metathesaurus to perform a semantic indexing and fusion of
medical media. This fusion operates before the query processing (retrieval) and
works at an UMLS-compliant conceptual indexing level. Our purpose is to study
various techniques related to semantic data alignment, preprocessing, fusion,
clustering and retrieval, by evaluating the various techniques and highlighting
future research directions. The alignment and the preprocessing are based on
partial text/image retrieval feedback and on the data structure. We analyze
various probabilistic, fuzzy and evidence-based approaches for the fusion
process and different similarity functions for the retrieval process. All the
proposed methods are evaluated on the Cross Language Evaluation Forum's (CLEF)
medical image retrieval benchmark, by focusing also on a more homogeneous
component medical image database: the Pathology Education Instructional
Resource (PEIR).
