%0 Book Section %1 noKey %A Glaßer, Sylvia %A Niemann, Uli %A Preim, Uta %A Preim, Bernhard %A Spiliopoulou, Myra %B Bildverarbeitung für die Medizin 2013 %D 2013 %E Meinzer, Hans-Peter %E Deserno, Thomas Martin %E Handels, Heinz %E Tolxdorff, Thomas %I Springer Berlin Heidelberg %K myown %P 45-50 %R 10.1007/978-3-642-36480-8_10 %T Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region %U http://dx.doi.org/10.1007/978-3-642-36480-8_10 %X 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. %@ 978-3-642-36479-2

@incollection{noKey, abstract = {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.}, added-at = {2014-08-07T11:55:42.000+0200}, author = {Glaßer, Sylvia and Niemann, Uli and Preim, Uta and Preim, Bernhard and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/244eb5dbac512bb3933d1f9b084199fcf/uli.niemann}, booktitle = {Bildverarbeitung für die Medizin 2013}, doi = {10.1007/978-3-642-36480-8_10}, editor = {Meinzer, Hans-Peter and Deserno, Thomas Martin and Handels, Heinz and Tolxdorff, Thomas}, interhash = {414fdba6151863aa96497da6a3d29217}, intrahash = {44eb5dbac512bb3933d1f9b084199fcf}, isbn = {978-3-642-36479-2}, keywords = {myown}, language = {German}, pages = {45-50}, publisher = {Springer Berlin Heidelberg}, series = {Informatik aktuell}, timestamp = {2014-08-07T11:55:42.000+0200}, title = {Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region}, url = {http://dx.doi.org/10.1007/978-3-642-36480-8_10}, year = 2013 }