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.
CITATION STYLE
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 Informatik aktuell (pp. 45–50). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-642-36480-8_10
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