Müller, H., Clough, P., Hersh, W.R. et al. (3 more authors) (2005) Evaluation axes for medical image retrieval systems: the imageCLEF experience. In: Zhang, H., Chua, T-S., Steinmetz, R., Kankanhalli, M.S. and Wilcox, L., (eds.) Proceedings of the 13th Annual ACM International Conference on Multimedia. ACM Multimedia 2005, 06-11 Nov 2005, Hilton, Singapore. ACM , 1014 - 1022. ISBN 1-59593-044-2
Abstract
Content--based image retrieval in the medical domain is an extremely hot topic in medical imaging as it promises to help better managing the large amount of medical images being produced. Applications are mainly expected in the field of medical teaching files and for research projects, where performance issues and speed are less critical than in the field of diagnostic aid. Final goal with most impact will be the use as a diagnostic aid in a real--world clinical setting.Other applications of image retrieval and image classification can be the automatic annotation of images with basic concepts or the control of DICOM header information.ImageCLEF is part of the Cross Language Evaluation Forum (CLEF). Since 2004, a medical image retrieval task has been added. Goal is to create databases of a realistic and useful size and also query topics that are based on real--world needs in the medical domain but still correspond to the limited capabilities of purely visual retrieval at the moment. Goal is to direct the research onto real applications and towards real clinical problems to give researchers who are not directly linked to medical facilities a possibility to work on the interesting problem of medical image retrieval based on real data sets and problems. The missing link between computer science research departments and clinical routine is one of the biggest problems that becomes evident when reading much of the current literature on medical image retrieval. Most databases are extremely small, the treated problems often far from clinical reality, and there is no integration of the prototypes into a hospital infrastructure. Only few retrieval articles specifically mention problems related to the DICOM format (Digital Imaging and Communications in Medicine) and the sheer amount of data that needs to be treated in an image archive ( > 30.000 images per day in the Geneva radiology).This article develops the various axes that can be taken into account for medical image retrieval system evaluation. First, the axes are developed based on current challenges and experiences from ImageCLEF. Then, the resources developed for ImageCLEF are listed and finally, the application of the axes is explained to show the bases of the ImageCLEFmed evaluation campaign. This article will only concentrate on the medical retrieval tasks, the non-medical tasks will only shortly be mentioned.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2005 ACM. This is an author produced version of a paper subsequently published in Proceedings of the 13th Annual ACM International Conference on Multimedia. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Benchmarking; Evaluation; Image retrieval; Medical image retrieval; Content-based image retrieval; |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Apr 2014 15:59 |
Last Modified: | 19 Dec 2022 13:26 |
Published Version: | http://dx.doi.org/10.1145/1101149.1101358 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/1101149.1101358 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:78476 |