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Application of unsupervised clustering methods to biomedical image analysis

Meyer-Bäse, A., Gruber, Peter, Theis, Fabian J., Wismüller, A. and Ritter, H. (2005) Application of unsupervised clustering methods to biomedical image analysis. In: Proc. of Workshop on Self-Organizing Maps (WSOM); Paris, France, 2005. , pp. 621-628.

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Unsupervised clustering techniques represent a powerful technique for self-organized segmentation of biomedical image time-series data describing groups of pixels exhibiting similar properties of local signal dynamics. The theoretical background is presented in the beginning, followed by several medical applications demonstrating the flexibility and conceptual power of these techniques. These ...


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Item type:Book section
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Dewey Decimal Classification:500 Science > 570 Life sciences
Created at the University of Regensburg:Unknown
Item ID:17317
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