1. A method for labeling connected tubular objects within segmented image data, comprising: receiving segmented image data, wherein the segmented image data includes pixels of original image data that are assigned labels such that pixels with the same label share certain visual characteristics; and labeling the segmented image data to identify a plurality of components within the segmented image data, wherein the labeling includes: processing the segmented image data to create a processed image that represents centerline and radii estimates of the connected tubular components to divide the components based on tubular diameter; determining seed point candidates in the processed image that are within a band of radii; grouping the candidates based on their physical distance from each other and their radii estimates; partitioning the segmented image data in accordance with the grouped candidates; and assigning a separate color label to each of the plurality of components that are different from each other, wherein the components include arteries and veins that are differentiated based on the labeling and the labeling is automatic and performed using a processor, and after labeling the segmented image data, the method further comprises: for each labeled component, reconnecting the component to another component and assigning a single label to the reconnected components based on an expected model of a branching structure, wherein the expected model classifies each component into one of a first group and a second group, the first group includes normal isolated tree components and the second group includes extraneous noise and other tree structures, and then, the expected model separates the components in the second group to ensure all components are only isolated portions of the tree.