Computer-aided diagnosis and visualization based on clustering and independent component analysis for breast MRI

Abstract

Computer-aided diagnosis and simultaneous visualization based on independent component analysis and clustering are integrated in an intelligent system for the evaluation of small mammographic lesions in breast MRI. These techniques are tested on biomedical time-series representing breast MRI scans and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional properties of contrast-agent uptake characterized by subtle differences of signal amplitude and dynamics, these methods provide both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provide a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Both approaches lead to an increase of the diagnostic accuracy of MRI mammography by improving the sensitivity without reduction of specificity.