Ontology of gaps in content-based image retrieval.
Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however,...
| Publicado en: | Journal of Digital Imaging Vol. 22; no. 2; pp. 202 - 216 |
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| Autores principales: | , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105483057&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105483057 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2009 vid: 22 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105483057 2010229053 10.1007/s10278-007-9092-x NLM18239964 105483057 ppf: 202 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Ontology of gaps in content-based image retrieval. aug: au: Deserno TM Antani S Long R affil: Department of Medical Informatics, Aachen University of Technology (RWTH), Pauwelsstr. 30, 52057, Aachen, Germany. deserno@ieee.org sug: subj: Image Retrieval Systems Image Retrieval Methods Picture Archiving and Communication Systems Abstracting and Indexing Methods Automation Classification Data Mining Nomenclature Systems Design ab: Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the 'semantic gap.' The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of 'gaps' in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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