Content-based queries on the casimage database within the IRMA framework

  • Authors:

  • Christian Thies;Mark Oliver Güld;Benedikt Fischer;Thomas M. Lehmann

  • Affiliations:

  • Department of Medical Informatics, University of Technology Aachen, Aachen, Germany;Department of Medical Informatics, University of Technology Aachen, Aachen, Germany;Department of Medical Informatics, University of Technology Aachen, Aachen, Germany;Department of Medical Informatics, University of Technology Aachen, Aachen, Germany

  • Venue:
  • CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
  • Year:
  • 2004

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Abstract

Recent research has suggested that there is no general similarity measure, which can be applied on arbitrary databases without any parameterization. Hence, the optimal combination of similarity measures and parameters must be identified for each new image repository. This optimization loop is time consuming and depends on the experience of the designer as well as the knowledge of the medical expert. It would be useful if results that have been obtained for one data set can be transferred to another without extensive re-design. This transfer is vital if content-based image retrieval is integrated into complex environments such as picture archiving and communication systems. The image retrieval in medical applications (IRMA) project defines a framework that strictly separates data administration and application logic. This permits an efficient transfer of the data abstraction of one database on another without re-designing the software. In the ImageCLEF competition, the query performance was evaluated on the CasImage data set without optimization of the feature combination successfully applied to the IRMA corpus. IRMA only makes use of basic features obtained from grey-value representations of the images without additional textual annotations. The results indicate that transfer of parameterization is possible without time consuming parameter adaption and significant loss of retrieval quality.