NDLI: Detection of suspicious lesions in dynamic contrast enhanced MRI data

Content Provider	IEEE Xplore Digital Library
Author	Twellmann, T. Saalbach, A. Muller, C. Nattkemper, T.W. Wismuller, A.
Copyright Year	2004
Description	Author affiliation: Appl. Neuroinformatics Group, Bielefeld Univ., Germany (Twellmann, T.; Saalbach, A.; Muller, C.; Nattkemper, T.W.)
Abstract	Dynamic contrast-enhanced magnet resonance imaging (DCE-MRI) has become an important source of information to aid breast cancer diagnosis. Nevertheless, next to the temporal sequence of 3D volume data from the DCE-MRI technique, the radiologist commonly adducts information from other modalities for his final diagnosis. Thus, the diagnosis process is time consuming and tools are required to support the human expert. We investigate an automatic approach that detects the location and delineates the extent of suspicious masses in multi-temporal DCE-MRI data sets. It applies the state-of-the-art support vector machine algorithm to the classification of the short-time series associated with each voxel. The ROC analysis shows an increased specificity in contrast to standard evaluations techniques.
Starting Page	454
Ending Page	457
File Size	321882
Page Count	4
File Format	PDF
ISBN	0780384393
DOI	10.1109/IEMBS.2004.1403192
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Magnetic resonance imaging Breast cancer Artificial neural networks Support vector machines Support vector machine classification X-ray imaging Visualization Data analysis Radiology Support Vector Machine Dynamic Contrast Enhanced MRI Multi-temporal Image Analysis Object Detection
Content Type	Text
Resource Type	Article

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