2008/11/28 by Roxana Teodorescu, Teodorescu, Roxana, Daniel Racoceanu +6
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Image and Video Retrieval Techniques #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.0811.4717
11 pages
arxiv created 2008/11/28 · openalex publication_date 2008/11/28 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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).