Co-occurrence of local anisotropic gradient orientations

摘要

Methods, apparatus, and other embodiments associated with distinguishing disease phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe) are described. One example apparatus includes a set of logics that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating disease pathology (e.g., cancer), computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, and classifies the phenotype of the disease pathology based on the feature vector. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image. Example methods and apparatus may operate substantially in real-time. Example methods and apparatus may operate in two or three dimensions.

主权项

1. A non-transitory computer-readable storage medium storing computer-executable instructions that when executed by a computer control the computer to perform a method for distinguishing disease phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe), 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, and 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; and controlling a phenotype classifier to classify the ROI based, at least in part, on the feature vector.