Abstract: This disclosure relates to diagnostic imaging of a retina of a patient with diabetic retinopathy. A processor retrieves a first image of the retina captured at a first point in time and a second image of the retina captured at a second point in time after the first point in time. The processor aligns the first image to the second image to reduce an offset between non-pathologic retina features in the first image and the second image and obtains image objects related to diabetic retinopathy in the first image and the second image. The processor then calculates a numerical pathology score indicative of a progression of the diabetic retinopathy by calculating a degree of change of the image objects related to diabetic retinopathy between the aligned first and second images and finally, creates an output representing the calculated numerical pathology score.

Abstract: Embodiments described herein provide a system and method for generating a binary descriptor representing an image patch (200). The method includes determining a first specified number of features within an image patch (200), performing mean intensity difference tests for the first specified number of features, and generating a binary string of the first specified number of features for the image patch (200) based on the mean intensity difference tests. The method includes dividing the image patch (200) into a number of sub-patches (204), and, for each sub-patch (204), determining a second specified number of features within the sub-patch (204), performing mean intensity difference tests for the second specified number of features, and generating a binary string of the second specified number of features. The method also includes transforming the binary strings for the image patch (200) and the sub-patches (204) into a binary descriptor representing the image patch (200).

Abstract: Embodiments described herein provide a system and method for generating a binary descriptor representing an image patch (200). The method includes determining a first specified number of features within an image patch (200), performing mean intensity difference tests for the first specified number of features, and generating a binary string of the first specified number of features for the image patch (200) based on the mean intensity difference tests. The method includes dividing the image patch (200) into a number of sub-patches (204), and, for each sub-patch (204), determining a second specified number of features within the sub-patch (204), performing mean intensity difference tests for the second specified number of features, and generating a binary string of the second specified number of features. The method also includes transforming the binary strings for the image patch (200) and the sub-patches (204) into a binary descriptor representing the image patch (200).