| Detection of suspicious lesions in dynamic contrast enhanced MRI data. | |
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MedLine Citation:
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PMID: 17271711 Owner: NLM Status: PubMed-not-MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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T Twellmann; A Saalbach; C Müller; T W Nattkemper; A Wismüller |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Volume: 1 ISSN: 1557-170X ISO Abbreviation: Conf Proc IEEE Eng Med Biol Soc Publication Date: 2004 |
Date Detail:
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Created Date: 2007-02-02 Completed Date: 2007-05-17 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101243413 Medline TA: Conf Proc IEEE Eng Med Biol Soc Country: United States |
Other Details:
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Languages: eng Pagination: 454-7 Citation Subset: - |
Affiliation:
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Appl. Neuroinformatics Group, Faculty of Technology, Bielefeld Univ., Germany. ttwellma@TechFak.Uni-Bielefeld.de |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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