de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Cluster analysis of biomedical image time-series

MPS-Authors

Wismüller,  A
Max Planck Institute of Psychiatry, Max Planck Society;

Lange,  O
Max Planck Institute of Psychiatry, Max Planck Society;

Dersch,  DR
Max Planck Institute of Psychiatry, Max Planck Society;

Leinsinger,  GL
Max Planck Institute of Psychiatry, Max Planck Society;

Hahn,  K
Max Planck Institute of Psychiatry, Max Planck Society;

Pütz,  B
Max Planck Institute of Psychiatry, Max Planck Society;

Auer,  D
Max Planck Institute of Psychiatry, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Wismüller, A., Lange, O., Dersch, D., Leinsinger, G., Hahn, K., Pütz, B., et al. (2002). Cluster analysis of biomedical image time-series. International Journal of Computer Vision, 46(2), 103-128.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-A24D-E
Abstract
In this paper, we present neural network clustering by deterministic annealing as a powerful strategy for self- organized segmentation of biomedical image time-series data identifying groups of pixels sharing common properties of local signal dynamics. After introducing the theoretical concept of minimal free energy vector quantization and related clustering techniques, we discuss its potential to serve as a multi- purpose computer vision strategy to image time-series analysis and visualization for many fields of medicine ranging from biomedical basic research to clinical assessment of patient data. In particular, we present applications to (i) functional MRI data analysis for human brain mapping, (ii) dynamic contrast-enhanced perfusion MRI for the diagnosis of cerebrovascular disease, and (iii) magnetic resonance mammography for the analysis of suspicious lesions in patients with breast cancer. This wide scope of completely different medical applications illustrates the flexibility and conceptual power of neural network vector quantization in this context. Although there are obvious methodological similarities, each application requires specific careful consideration w.r.t. data preprocessing, postprocessing and interpretation. This challenge can only be managed by close interdisciplinary cooperation of medical doctors, engineers, and computer scientists. Hence, this field of research can serve as an example for lively cross-fertilization between computer vision and related researc