Abstract
Classification of breast tumors in perfusion DCE-MRI solely based on dynamic contrast enhanced magnetic resonance data is a challenge. Many studies employ grouping of voxels into regions via clustering for further analysis. However, the clustering result strongly depends on the chosen clustering algorithm and its parameter settings. In this paper, we explain how spectral clustering can be adapted to breast tumor data and suggest how the clustering parameters can be automatically derived such that no pre-defined user input, e.g., cluster number, is necessary. The presented spectral clustering approach has the great advantage of generating spatially connected regions. Furthermore, it can be enabled for automatic classification and yields similar results as previous approaches.
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Glaßer, S., Roscher, S., Preim, B. (2014). Adapted Spectral Clustering for Evaluation and Classification of DCE-MRI Breast Tumors. In: Deserno, T., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2014. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54111-7_39
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DOI: https://doi.org/10.1007/978-3-642-54111-7_39
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