1. A method, implemented via a computing device, for generating a characteristic map of perfusion characteristics of cardiac tissue from at least one radiology image comprising the steps of: a. Retrieving a contrast-enhanced perfusion sequence using a computing device, wherein said perfusion sequence is generated using a radiology imaging modality and wherein said perfusion sequence comprises: a plurality of two-dimensional image slices comprising a three-dimensional volume image and a time series for each of said two-dimensional image slices, and wherein each pixel or voxel of each two-dimensional image slice comprises a signal intensity proportional to perfusion at that location in the cardiac tissue; b. Applying in-plane motion correction using said computing device to align position of the cardiac tissue using said time series for each of said image slices; c. Cropping a first region of interest of said contrast-enhanced perfusion images using said computing device, wherein said first region of interest comprises a first plurality of pixels or voxels; d. Segmenting out at least one additional region of interest from said first region of interest, wherein said additional region of interest comprises a second plurality of pixels or voxels and wherein said segmenting is further achieved by applying a shape detection algorithm comprising the steps of: detecting a mid-line of the left ventricular myocardium for each two-dimensional image slice, identifying a centroid and a radius of the left ventricular myocardium mid-line using a Circular High Transform, wherein the Circular High Transform iteratively operates on each resulting pixel or voxel corresponding to the left ventricular myocardium mid-line, identifying a mean left ventricular myocardial wall thickness by applying a canny edge detection algorithm to thereby mark the endocardium and the epicardium of the left ventricular myocardium and calculate an average distance between the centroid and a plurality of circumferential points for each two-dimensional image slice, scaling the detected left ventricular myocardial mid-line from the detected centroid using the identified wall thickness to thereby demarcate the endocardium using a circular shape approximation, wherein the circular shape approximation further comprises at least one radial tolerance limit to thereby exclude pixel data or voxel data corresponding to regions outside of the left ventricular myocardium from the region of interest in each two-dimensional image slice; e. generating at least one characteristic map of said perfusion characteristics using a computing device, wherein the characteristic map defines at-risk and normal perfusion areas, wherein generating further comprises: subdividing each region of interest into a plurality of subregions, generating at least one expected normal perfusion update curve by applying a physics-based image processing technique, wherein the physics-based image processing technique further comprises a patient-specific numerical advection diffusion blood transport model, and comparing, using a computing device, a plurality of observed perfusion uptake curves with at least one expected normal perfusion uptake curve to thereby generate the characteristic map of perfusion characteristics for at least one of said subregions; identifying at least one subregion comprising at least one at risk perfusion area, applying at least one physics-based image processing technique to at least one pixel or voxel in at least one at risk region of interest to confirm the perfusion characteristic of the pixel or voxel as comprising at least one of: a normal perfusion area and an at risk perfusion area; and f. Displaying at least one visual representation of said perfusion characteristics of the portion of the heart on a clinician device for review by said clinician to thereby enable the clinician in generating at least one diagnosis based on the perfusion characteristics, wherein said visual representation is a two-dimensional visual representation or a three-dimensional visual representation and further comprises a color map, wherein said color map is representative of the matching of observed perfusion uptake curves to at least one modeled normal perfusion uptake curve obtained from the a patient-specific numerical advection-diffusion blood transport model using at least one standard estimation method, and wherein said displaying further comprises superimposing a polar plot over the visual representation.