title: Multiscale analysis of MR-mammography data.
abstract: In this work we propose a method for automatically discriminating between different types of tissue in MR mammography datasets. This is accomplished by employing a wavelet-based multiscale analysis. After the data has been wavelet-transformed unsupervised machine learning methods are employed to identify typical patterns in the wavelet domain. To demonstrate the potential of the proposed approach we apply a filtering procedure that extracts the wavelet-based image information related to tumour tissue. In this way we obtain a robust segmentation of suspicious tissue in the MR image.
authors: Theoretische Physik, Universität Bielefeld, Germany. lessmann@physik.uni-bielefeld.de
authors: Birgit
authors: B
authors: Lessmann
authors: Tim W
authors: TW
authors: Nattkemper
authors: Preminda
authors: P
authors: Kessar
authors: Linda
authors: L
authors: Pointon
authors: Michael
authors: M
authors: Khazen
authors: Martin O
authors: MO
authors: Leach
authors: Andreas
authors: A
authors: Degenhard
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D004502:Education, Medical, Continuing; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008327:Mammography / Q000379:methods*; D015203:Reproducibility of Results