1. A machine classifier based method of diagnosing glaucoma in a subject, comprising: obtaining OCT images from three anatomic regions of the subject's eye, wherein said three anatomic regions consists of the mGCC, the ppNFL, and the ONH; processing the images to obtain a predetermined collection of diagnostic parameters, wherein said collection of diagnostic parameters comprises the following characteristics: a. each parameter is classified as belonging to one of the three anatomic regions, b. in cases where the parameter is a pattern cross-correlation parameter, it is further classified as one of a GPCC type selected from superior, inferior, and even, and c. at least the mGCC and the ppNFL regions are represented in the collection of diagnostic parameters; and transforming the collection of diagnostic parameters by combining them across anatomic regions and GPCC types according to a predetermined combination formula to arrive at a set of reduced global parameters; and applying a pre-trained machine classifier to the reduced global parameters to arrive at a single indicator for the subject's glaucoma status.