Meyer-Bäse, A. and Gruber, Peter and Theis, Fabian J. and 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. UNSPECIFIED, 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 applications range from functional MRI data analysis to dynamic contrast-enhanced perfusion MRI and breast MRI. The present paper gives a review of potential applications of unsupervised clustering techniques in the important and current field of functional and dynamic MRI.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||15 Oct 2010 08:58|
|Last Modified:||15 Oct 2010 08:58|