Breast MRI data analysis by LLE - Varini, C.; Nattkemper, T.W.; Degenhard, A.; Wismuller, A. (2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004)
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.