A computer-implemented method of determining a level of a pathology associated with a disease being one of diabetic retinopathy and age-related macular degeneration that causes lesions on a retina of a subject, the level of the pathology being determined from an image of the retina of the subject in which the lesions are visible, the image having been captured using an ocular imaging system, the method comprising: acquiring a plurality of lesion locations in the image from an automated lesion detector which has identified the plurality of lesion locations in the image; applying a clustering algorithm to the plurality of lesion locations such that the clustering algorithm identifies at least one lesion cluster and corresponding lesion cluster data; categorizing each of the at least one lesion cluster into one of a set of predetermined categories based on the identified lesion cluster data, by using a grid to define fields (F1 to F7, P1 to P5) in the image of the retina, and categorizing each of the at least one lesion cluster according to the field in which a centroid, a mean location of lesions of the at least one lesion cluster or a median location of the lesions of the at least one lesion cluster is located; applying at least one function to the lesion cluster data with regard to each category of the set of predetermined categories, wherein the at least one function provides a fixed number of data outputs, and is a statistical function which takes a set of real numbers and returns a real number; and determining a level of pathology from the image by processing the fixed number of data outputs of each category of the set of predetermined categories using a classification algorithm, wherein the classification algorithm is built by a learning algorithm from an example training set comprising first image data (701; 801, 802, 803) defining images of a portion of the retina of a plurality of subjects in which the lesions are present and indicate a first level of pathology associated with the disease, and second image data (702; 804) defining images of a portion of the retina of a plurality of subjects in which the lesions are present and indicate a second, different level of pathology or an absence of a pathology associated with the disease. acquiring a plurality of lesion locations in the image from an automated lesion detector which has identified the plurality of lesion locations in the image; applying a clustering algorithm to the plurality of lesion locations such that the clustering algorithm identifies at least one lesion cluster and corresponding lesion cluster data; categorizing each of the at least one lesion cluster into one of a set of predetermined categories based on the identified lesion cluster data, by using a grid to define fields (F1 to F7, P1 to P5) in the image of the retina, and categorizing each of the at least one lesion cluster according to the field in which a centroid, a mean location of lesions of the at least one lesion cluster or a median location of the lesions of the at least one lesion cluster is located; applying at least one function to the lesion cluster data with regard to each category of the set of predetermined categories, wherein the at least one function provides a fixed number of data outputs, and is a statistical function which takes a set of real numbers and returns a real number; and determining a level of pathology from the image by processing the fixed number of data outputs of each category of the set of predetermined categories using a classification algorithm, wherein the classification algorithm is built by a learning algorithm from an example training set comprising first image data (701; 801, 802, 803) defining images of a portion of the retina of a plurality of subjects in which the lesions are present and indicate a first level of pathology associated with the disease, and second image data (702; 804) defining images of a portion of the retina of a plurality of subjects in which the lesions are present and indicate a second, different level of pathology or an absence of a pathology associated with the disease.