Zusammenfassung
Classification of breast tumors solely based on dynamic contrast enhanced magnetic resonance data is a challenge in clinical research. In this paper, we analyze how the most suspect region as group of similarly perfused and spatially connected voxels of a breast tumor contributes to distinguishing between benign and malignant tumors. We use three density-based clustering algorithms to partition a tumor in regions and depict the most suspect one, as delivered by the most stable clustering algorithm. We use the properties of this region for each tumor as input to a classifier. Our preliminary results show that the classifier separates between benign and malignant tumors, and returns predictive attributes that are intuitive to the expert.
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Glaßer, S., Niemann, U., Preim, U., Preim, B., Spiliopoulou, M. (2013). Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region. In: Meinzer, HP., Deserno, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2013. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36480-8_10
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DOI: https://doi.org/10.1007/978-3-642-36480-8_10
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