System and method for automated segmentation, characterization, and classification of possibly malignant lesions and stratification of malignant tumors

A system for classifying a possibly malignant or nonmalignant region of interest (ROI), comprising:

• a processor, having an input port configured to receive dynamic contrast enhanced (DCE) MRI images, the processor being configured to:
• receive a plurality of ROI slices, each ROI slice representing a respective one of the series of time periods and corresponding to one of the sets of DCE MRI images, each of the ROI slices including a plurality of voxels representative of the possibly malignant or non-malignant ROI in the corresponding time period;
• automatically extract a boundary of the ROI from each respective received ROI slice in the series of time periods, by processing the voxels in the received ROI slice according to an expectation/maximization algorithm to group the voxels and by processing the grouped voxels using a magnetostatic active contour model to extract the boundary of the ROI;
• for each ROI slice, determine texture features of the voxels within the boundary of the ROI, to the exclusion of voxels outside of the boundary;
• determine kinetic texture features of the ROI based on the respective extracted boundary of the ROI and the texture features in each ROI slice, the kinetic texture features being representative of spatio-temporal changes in the texture features over each of the voxels in the ROI over the series of time periods, and
• apply a classifier to the kinetic texture features to classify the ROI as a whole as belonging to one of at least two classes.