1. A method for classifying multiple voxels within dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) images acquired over a series of time periods, the method comprising: receiving a 2D or 3D image for each time period from among the corresponding set of DCE MRI images, the image representing a plurality of voxels having a respective plurality of voxel locations, the plurality of voxels including multiple voxels in a region of interest (ROI) being representative of the ROI in the corresponding time period; determining 2D or 3D textural features for at least the multiple voxels in the ROI in each image at each time period in the series of time periods; determining kinetic texture features for each of the voxel locations in the ROI across all time periods in the series of time periods to represent spatio-temporal changes in the textural features of each of the voxels in the ROI over the series of time periods; fitting a mathematical model to the determined kinetic texture features representing the changes in the textural features of each of the voxel locations in the ROI; determining parameter values of the mathematical model that is fitted to the changes in texture for each of the voxel locations in the ROI by selecting individual unpaired coefficients from the mathematical model as the determined parameters; applying a classifier to the determined kinetic texture features and to the determined parameters of the mathematical model of changes in spatio-temporal texture to identify an abnormality in the ROI and to classify the abnormality into one of at least two classes; and visually indicating the classified abnormality as determined by the classifier for at least a portion of ROI in the image.