Content-based image analysis: object extraction by data-mining on hierarchically decomposed medical images

Christian J. Thies; Volker H. Metzler; Til Aach

doi:10.1117/12.481378

15 May 2003 Content-based image analysis: object extraction by data-mining on hierarchically decomposed medical images

Christian J. Thies, Volker H. Metzler, Til Aach

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481378
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Christian J. Thies, Volker H. Metzler, and Til Aach "Content-based image analysis: object extraction by data-mining on hierarchically decomposed medical images", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481378

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