A framework and baseline results for the CLEF medical automatic annotation task

Authors:
Mark O. Güld;Christian Thies;Benedikt Fischer;Thomas M. Deserno
Affiliations:
Department of Medical Informatics, RWTH Aachen University, Pauwelsstraíe 30, 52057 Aachen, Germany;Department of Medical Informatics, RWTH Aachen University, Pauwelsstraíe 30, 52057 Aachen, Germany;Department of Medical Informatics, RWTH Aachen University, Pauwelsstraíe 30, 52057 Aachen, Germany;Department of Medical Informatics, RWTH Aachen University, Pauwelsstraíe 30, 52057 Aachen, Germany
Venue:
Pattern Recognition Letters
Year:
2008

Citing 6
Cited 0

Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Progressive search and retrieval in large image archives

IBM Journal of Research and Development - Papers on mustimedia systems
Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion

Pattern Recognition Letters
Content-based retrieval of medical images by combining global features

CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Content-based queries on the casimage database within the IRMA framework

CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Baseline results for the ImageCLEF 2006 medical automatic annotation task

CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval

Quantified Score

Hi-index	0.10

Visualization

Abstract

The medical automatic annotation task issued by the cross language evaluation forum (CLEF) aims at a fair comparison of state-of-the art algorithms for medical content-based image retrieval (CBIR). The contribution of this work is twofold: at first, a logical decomposition of the CBIR task is presented, and key elements to support the relevant steps are identified: (i) implementation of algorithms for feature extraction, feature comparison, and classifier combination, (ii) visualization of extracted features and retrieval results, (iii) generic evaluation of retrieval algorithms, and (iv) optimization of the parameters for the retrieval algorithms and their combination. Data structures and tools to address these key elements are integrated into an existing framework for image retrieval in medical applications (IRMA). Secondly, baseline results for the CLEF annotation tasks 2005-2007 are provided applying the IRMA framework, where global features and corresponding distance measures are combined within a nearest neighbor approach. Using identical classifier parameters and combination weights for each year shows that the task difficulty decreases over the years. The declining rank of the baseline submission also indicates the overall advances in CBIR concepts. Furthermore, a rough comparison between participants who submitted in only one of the years becomes possible.