A method of determining risk of heart disease in a patient, the method comprising: receiving a first image of a breast of the patient obtained during a mammogram; removing noise from the first image thereby creating a de-noised image; creating a search unit for searching the de-noised image; creating one or more search points on the de-noised image; interpolating, for each search unit associated with a search point, pixel intensity from the de-noised image onto the search unit; computing one or more points of interest on the de-noised image based on the interpolation; creating one or more mesoscale regions of interest on the de-noised image; characterized in that the method further comprises: computing a connectivity for each of the one or more mesoscale regions of interest, wherein computing the connectivity includes computing the number of points of interest included within the mesoscale region of interest, computing a connectivity index for the region of interest, and recording neighboring mesoscale regions of interest connected to the mesoscale region of interest; identifying one or more connected components using the computed connectivity, wherein each connected component includes a set of mesoscale regions of interest in which each region of interest contained therein has at least one nearest neighbor also contained in the set of mesoscale regions of interest and no nearest neighbor from a different set of mesoscale regions of interest, and wherein each of the one or more connected components represents a branch of a global curvilinear structure; computing one or more physical properties of the one or more connected components; selecting a set of branches based on the computed physical properties for each branch of the global curvilinear structure; pruning each branch in the selected set of branches based on an error-tolerant, adaptive polynomial fit; identifying remaining regions of interest in each pruned branch; identifying remaining points of interest in the remaining regions of interest; and growing a chain formed by the remaining points of interest, wherein the chain represents a macroscopic, global curvilinear calcified arterial structure, wherein the detected calcified arterial structures are quantitated, and wherein quantitated values for calcified arterial structures indicate risk of heart disease. receiving a first image of a breast of the patient obtained during a mammogram; removing noise from the first image thereby creating a de-noised image; creating a search unit for searching the de-noised image; creating one or more search points on the de-noised image; interpolating, for each search unit associated with a search point, pixel intensity from the de-noised image onto the search unit; computing one or more points of interest on the de-noised image based on the interpolation; creating one or more mesoscale regions of interest on the de-noised image; characterized in that the method further comprises: computing a connectivity for each of the one or more mesoscale regions of interest, wherein computing the connectivity includes computing the number of points of interest included within the mesoscale region of interest, computing a connectivity index for the region of interest, and recording neighboring mesoscale regions of interest connected to the mesoscale region of interest; identifying one or more connected components using the computed connectivity, wherein each connected component includes a set of mesoscale regions of interest in which each region of interest contained therein has at least one nearest neighbor also contained in the set of mesoscale regions of interest and no nearest neighbor from a different set of mesoscale regions of interest, and wherein each of the one or more connected components represents a branch of a global curvilinear structure; computing one or more physical properties of the one or more connected components; selecting a set of branches based on the computed physical properties for each branch of the global curvilinear structure; pruning each branch in the selected set of branches based on an error-tolerant, adaptive polynomial fit; identifying remaining regions of interest in each pruned branch; identifying remaining points of interest in the remaining regions of interest; and growing a chain formed by the remaining points of interest, wherein the chain represents a macroscopic, global curvilinear calcified arterial structure, wherein the detected calcified arterial structures are quantitated, and wherein quantitated values for calcified arterial structures indicate risk of heart disease.