NDLI: Can we distinguish between benign and malignant breast tumors in DCE-MRI by studying a tumor's most suspect region only?

Content Provider	IEEE Xplore Digital Library
Author	Glasser, S. Niemann, U. Preim, B. Spiliopoulou, M.
Copyright Year	2013
Description	Author affiliation: Knowledge Manage. & Discovery Lab. (KMD), Univ. Magdeburg, Magdeburg, Germany (Spiliopoulou, M.) \|\| Dept. for Simulation & Graphics, Univ. Magdeburg, Magdeburg, Germany (Glasser, S.; Niemann, U.; Preim, B.)
Abstract	We investigate the task of breast tumor classification based on dynamic contrast-enhanced magnetic resonance image data (DCE-MRI). Our objective is to study how the formation of regions of similar voxels contributes to distinguishing between benign and malignant tumors. First, we perform clustering on each tumor with different algorithms and parameter settings, and then combine the clustering results to identify the most suspect region of the tumor and derive features from it. With these features we train classifiers on a set of tumors that are difficult to classify, even for human experts. We show that the features of the most suspect region alone cannot distinguish between benign and malignant tumors, yet the properties of this region are indicative of tumor malignancy for the dataset we studied.
Starting Page	77
Ending Page	82
File Size	996021
Page Count	6
File Format	PDF
ISBN	9781479910533
DOI	10.1109/CBMS.2013.6627768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-20
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Cancer Breast tumors Clustering algorithms Decision trees Malignant tumors
Content Type	Text
Resource Type	Article

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