RESEARCH ARTICLE


Prototypes for Content-Based Image Retrieval in Clinical Practice



Adrien Depeursinge*, 1, 2, Benedikt Fischer 3, Henning Müller 1, 2, Thomas M Deserno 3
1 Business Information Systems, University of Applied Sciences Western Switzerland (HES–SO), TechnoArk 3, 3960 Sierre, Switzerland
2 Service of Medical Informatics, University and University Hospitals of Geneva (HUG), Rue Gabrielle–Perret–Gentil 4,1211 Geneva 14, Switzerland
3 Department of Medical Informatics, RWTH Aachen University, Pauwelsstr. 30, D-52057 Aachen, Germany


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© Depeursinge et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Business Information Systems, University of Applied Sciences Western Switzerland (HES–SO), TechnoArk 3, 3960 Sierre, Switzerland; Tel +41 27 606 9023; Fax +41 27 606 9000; E-mail: adrien.depeursinge@hevs.ch


Abstract

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice.

We define applicability to clinical practice by having recently demonstrated the CBIR system on one of the CAD demonstration workshops held at international conferences, such as SPIE Medical Imaging, CARS, SIIM, RSNA, and IEEE ISBI. From 2009 to 2011, the programs of CADdemo@CARS and the CAD Demonstration Workshop at SPIE Medical Imaging were sought for the key word “retrieval” in the title. The systems identified were analyzed and compared according to the hierarchy of gaps for CBIR systems.

In total, 70 software demonstrations were analyzed. 5 systems were identified meeting the criterions. The fields of application are (i) bone age assessment, (ii) bone fractures, (iii) interstitial lung diseases, and (iv) mammography. Bridging the particular gaps of semantics, feature extraction, feature structure, and evaluation have been addressed most frequently.

In specific application domains, CBIR technology is available for clinical practice. While system development has mainly focused on bridging content and feature gaps, performance and usability have become increasingly important. The evaluation must be based on a larger set of reference data, and workflow integration must be achieved before CBIR-CAD is really established in clinical practice.

Keywords: Content-based image retrieval, medical image retrieval, diagnosis aid, prototypes.