Multiscale analysis of MR-mammography data.
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.
Lessmann
Birgit
B
Theoretische Physik, Universität Bielefeld, Germany. lessmann@physik.uni-bielefeld.de
Nattkemper
Tim W
TW
Kessar
Preminda
P
Pointon
Linda
L
Khazen
Michael
M
Leach
Martin O
MO
Degenhard
Andreas
A
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