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Proceedings Paper

Content-based image analysis: object extraction by data-mining on hierarchically decomposed medical images
Author(s): Christian J. Thies; Volker H. Metzler; Til Aach
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Paper Abstract

Reliable automated analysis and examination of biomedical images requires reproducible and robust extraction of contained image objects. However, the necessary description of image content as visually relevant objects is context-dependent and determined by parameters such as resolution, orientation, and, of course, the clinical-diagnostic question. Therefore a computer-based approach has to model both examination context and image acquisition as expert knowledge. Generally, static solutions are not satisfying because a change of application will most likely require a redesign of the analysis process. In contrast to non-satisfying statical solution, this paper describes a flexible approach, which allows medical examiners the context-sensitive extraction of sought objects from almost arbitrary medical images, without requiring technical knowledge on image analysis and processing. Since this methodology is applicable to any analysis task on large image sets, it works for general image series analysis as well as image retrieval. The new approach combines classical image analysis with the idea of data mining to close the gap between low abstraction on the technical level and high-level expert knowledge on image content and understanding.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481378
Show Author Affiliations
Christian J. Thies, RWTH-Aachen (Germany)
Volker H. Metzler, Univ. of Luebeck (Germany)
Til Aach, Univ. of Luebeck (Germany)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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