Paper Info
Title
Evaluation axes for medical image retrieval systems: the imageCLEF experience
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.
Year
DOI
Venue
2005
10.1145/1101149.1101358
ACM Multimedia 2001
Keywords
Field
DocType
imageclef experience,evaluation axis,medical image retrieval system,medical image retrieval task,medical image retrieval,medical retrieval task,medical facility,image retrieval,medical domain,medical imaging,medical image,medical teaching file,benchmarking,evaluation,image classification
Medical imaging,Computer science,Image retrieval,Artificial intelligence,Contextual image classification,Computer vision,Automatic image annotation,DICOM,Annotation,Information retrieval,Multimedia,Content-based image retrieval,Visual Word
Conference
ISBN
Citations 
PageRank 
1-59593-044-2
10
0.73
References 
Authors
17
6
Name
Order
Citations
PageRank
Henning Müller12538218.89
Paul Clough21308111.91
William Hersh32491307.00
Thomas Deselaers43569203.12
Thomas Lehmann5100.73
Antoine Geissbuhler681549.75