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Proceedings Paper

Content-based image retrieval from a database of fracture images
Author(s): Henning Müller; Phuong Anh Do Hoang; Adrien Depeursinge; Pierre Hoffmeyer; Richard Stern; Christian Lovis; Antoine Geissbuhler
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Paper Abstract

This article describes the use of a medical image retrieval system on a database of 16'000 fractures, selected from surgical routine over several years. Image retrieval has been a very active domain of research for several years. It was frequently proposed for the medical domain, but only few running systems were ever tested in clinical routine. For the planning of surgical interventions after fractures, x-ray images play an important role. The fractures are classified according to exact fracture location, plus whether and to which degree the fracture is damaging articulations to see how complicated a reparation will be. Several classification systems for fractures exist and the classification plus the experience of the surgeon lead in the end to the choice of surgical technique (screw, metal plate, ...). This choice is strongly influenced by the experience and knowledge of the surgeons with respect to a certain technique. Goal of this article is to describe a prototype that supplies similar cases to an example to help treatment planning and find the most appropriate technique for a surgical intervention. Our database contains over 16'000 fracture images before and after a surgical intervention. We use an image retrieval system (GNU Image Finding Tool, GIFT) to find cases/images similar to an example case currently under observation. Problems encountered are varying illumination of images as well as strong anatomic differences between patients. Regions of interest are usually small and the retrieval system needs to focus on this region. Results show that GIFT is capable of supplying similar cases, particularly when using relevance feedback, on such a large database. Usual image retrieval is based on a single image as search target but for this application we have to select images by case as similar cases need to be found and not images. A few false positive cases often remain in the results but they can be sorted out quickly by the surgeons. Image retrieval can well be used for the planning of operations by supplying similar cases. A variety of challenges has been identified and partly solved (varying luminosity, small region of interested, case-based instead of image-based). This article mainly presents a case study to identify potential benefits and problems. Several steps for improving the system have been identified as well and will be described at the end of the paper.

Paper Details

Date Published: 21 March 2007
PDF: 11 pages
Proc. SPIE 6516, Medical Imaging 2007: PACS and Imaging Informatics, 65160H (21 March 2007); doi: 10.1117/12.709516
Show Author Affiliations
Henning Müller, Univ. Hospitals of Geneva (Switzerland)
Phuong Anh Do Hoang, Univ. Hospitals of Geneva (Switzerland)
Adrien Depeursinge, Univ. Hospitals of Geneva (Switzerland)
Pierre Hoffmeyer, Univ. Hospitals of Geneva (Switzerland)
Richard Stern, Univ. Hospitals of Geneva (Switzerland)
Christian Lovis, Univ. Hospitals of Geneva (Switzerland)
Antoine Geissbuhler, Univ. Hospitals of Geneva (Switzerland)


Published in SPIE Proceedings Vol. 6516:
Medical Imaging 2007: PACS and Imaging Informatics
Steven C. Horii; Katherine P. Andriole, Editor(s)

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