Breast MRI data analysis by LLE

Author

Varini, Claudia ; Nattkemper, Tim W. ; Degenhard, Andreas ; Wismuller, Axel

Author_Institution

Dept. of Phys., Bielefeld Univ., Germany

Volume

3

fYear

2004

fDate

25-29 July 2004

Firstpage

2449

Abstract

Locally linear embedding (LLE) has recently been proposed as a powerful algorithm for unsupervised learning and dimensional data reduction. For a first time we apply LLE to a problem of medical data analysis. Magnetic resonance imaging (MRI) is considered as an essential imaging modality in the detection and classification of breast cancer. In dynamic contrast enhanced MRI (DCE-MRI) the data set of each patient is composed of a sequence of images and each data point in the image is associated with one time-series feature vector. Our results show that LLE is capable of revealing the heterogeneity of malignant tumors from the data structure of DCE-MRI signals.

Keywords

biomedical MRI; cancer; data reduction; feature extraction; image classification; image sequences; medical image processing; medical signal detection; time series; tumours; unsupervised learning; breast MRI data analysis; breast cancer classification; breast cancer detection; data structure; dimensional data reduction; dynamic contrast enhanced imaging; image sequence; locally linear embedding; magnetic resonance imaging; malignant tumors; medical data analysis; medical signals; time series feature vector; unsupervised learning; Benign tumors; Biomedical imaging; Breast cancer; Cancer detection; Data analysis; Magnetic resonance imaging; Malignant tumors; Mammography; Physics; Protocols;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1381012

Filename

1381012