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. 2006:2006:674-8.

Combining image features, case descriptions and UMLS concepts to improve retrieval of medical images

Miguel E Ruiz  1
Affiliations

Combining image features, case descriptions and UMLS concepts to improve retrieval of medical images

Miguel E Ruiz. AMIA Annu Symp Proc. 2006.

Abstract

This paper evaluates a system, UBMedTIRS, for retrieval of medical images. The system uses a combination of image and text features as well as mapping of free text to UMLS concepts. UBMedTIRS combines three publicly available tools: a content-based image retrieval system (GIFT), a text retrieval system (SMART), and a tool for mapping free text to UMLS concepts (MetaMap). The system is evaluated using the ImageCLEFmed 2005 collection that contains approximately 50,000 medical images with associated text descriptions in English, French and German. Our experimental results indicate that the proposed approach yields significant improvements in retrieval performance. Our system performs 156% above the GIFT system and 42% above the text retrieval system.

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Figures

Figure 1
Figure 1
Architecture of UBMedTIRS
Figure 2
Figure 2
Recall Precision Graph
Figure 3
Figure 3
Query by query difference in performance with respect to the CBIR baseline
Figure 4
Figure 4
Difference in performance with respect to the Text baseline
See this image and copyright information in PMC

References

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    1. Clough P, Müller H, Deselaers T, et al. The CLEF 2005 Cross-Language Image Retrieval Track. In: Peters C, Magni R, editors. Cross Language Evaluation Forum 2005. Vienna, Austria: Springer; 2006.
    1. Ruiz ME, Srikanth M. UB at CLEF2004 Cross Language Medical Image Retrieval; Paper presented at: Fifth Workshop of the Cross-Language Evaluation Forum (CLEF 2004), 2005; Bath, England.

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