Visual Exploratory Analysis of DCE-MRI Data in Breast Cancer Based on Novel Nonlinear Dimensional Data Reduction Techniques

摘要

Visualization of multi-dimensional data sets becomes a critical and significant area in modern medical imageprocessing. To analyze such high dimensional data, novel nonlinear embedding approaches become increasinglyimportant to show dependencies among these data in a two- or three-dimensional space. This paper investigatesthe potential of novel nonlinear dimensional data reduction techniques and compares their results with provennonlinear techniques when applied to the differentiation of malignant and benign lesions described by high-dimensional data sets arising from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Twoimportant visualization modalities in medical imaging are presented: the mapping on a lower-dimensional datamanifold and the image fusion.