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Methods Inf Med 2004; 43(04): 308-314
DOI: 10.1055/s-0038-1633873
Editorial
Schattauer GmbH

Advances in Biomedical Image Analysis

Past, Present and Future Challenges
T. M. Lehmann
1   Department of Medical Informatics, Aachen University of Technology (RWTH), Aachen, Germany
,
H. P. Meinzer
2   Department of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
,
T. Tolxdorff
3   Institute for Medical Informatics, Biostatistics and Epidemiology, Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
05 February 2018 (online)

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Summary

Starting from raw data files coding eight bits of gray values per image pixel and identified with no more than eight characters to refer to the patient, the study, and technical parameters of the imaging modality, biomedical imaging has undergone manifold and rapid developments. Today, rather complex protocols such as Digital Imaging and Communications in Medicine (DICOM) are used to handle medical images. Most restrictions to image formation, visualization, storage and transfer have basically been solved and image interpretation now sets the focus of research. Currently, a method-driven modeling approach dominates the field of biomedical image processing, as algorithms for registration, segmentation, classification and measurements are developed on a methodological level. However, a further metamorphosis of paradigms has already started. The future of medical image processing is seen in task-oriented solutions integrated into diagnosis, intervention planning, therapy and followup studies. This alteration of paradigms is also reflected in the literature. As German activities are strongly tied to the international research, this change of paradigm is demonstrated by selected papers from the German annual workshop on medical image processing collected in this special issue.

Keywords

Computer-assisted image processing - computer-assisted image interpretation - automatic data processing - medical informatics applications - pattern recognition - three-dimensional imaging
 

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