  Non-mass enhancing lesions (NME) constitute a diagnostic challenge in dynamic
contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer
Aided Diagnosis (CAD) systems provide physicians with advanced tools for
analysis, assessment and evaluation that have a significant impact on the
diagnostic performance. Here, we propose a new approach to address the
challenge of NME detection and segmentation, taking advantage of independent
component analysis (ICA) to extract data-driven dynamic lesion
characterizations. A set of independent sources was obtained from DCE-MRI
dataset of breast patients, and the dynamic behavior of the different tissues
was described by multiple dynamic curves, together with a set of eigenimages
describing the scores for each voxel. A new test image is projected onto the
independent source space using the unmixing matrix, and each voxel is
classified by a support vector machine (SVM) that has already been trained with
manually delineated data. A solution to the high false positive rate problem is
proposed by controlling the SVM hyperplane location, outperforming previously
published approaches.
