1. A non-transitory computer-readable storage device storing computer-executable instructions that when executed by a computer controls the computer to perform a method for distinguishing breast cancer phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe) and Laws energy features, the method comprising: accessing a region of interest (ROI) in a volume illustrated in a magnetic resonance image (MRI), the ROI having a set of pixels, where a pixel in the set of pixels has an intensity; obtaining an x-axis gradient for a first pixel in the set of pixels based, at least in part, on the intensity of the pixel; obtaining a y-axis gradient for the first pixel based, at least in part, on the intensity of the pixel; computing an x-axis gradient vector for a second pixel in an N pixel by N pixel neighborhood centered around the first pixel, N being a number; computing a y-axis gradient vector for the second pixel in the N pixel by N pixel neighborhood; constructing a localized gradient vector matrix based, at least in part, on the x-axis gradient vector for the second pixel, and the y-axis gradient vector for the second pixel; computing a dominant orientation for the first pixel based, at least in part, on the localized gradient vector matrix; constructing a co-occurrence matrix from the dominant orientation; computing an entropy for the first pixel based, at least in part, on the co-occurrence matrix; obtaining a distribution of the entropy; constructing a feature vector based, at least in part, on the distribution of the entropy; extracting a set of texture features from the ROI; and controlling a phenotype classifier to classify the ROI based, at least in part, on the feature vector and the set of texture features.