A computerized image registration process, comprising: a. selecting a first digital image of a first tissue section from a set of digital images of adjacent tissue sections of a single patient; b. selecting a second digital image of a second tissue section from the set; c. matching tissue structure between the first digital image and the second digital image, wherein matching the tissue structure comprises a coarse registration mode, wherein matching the tissue structure further comprises a fine registration mode to refine alignment of the first digital image and the second digital image; and d. automatically mapping an annotation drawn on the first digital image to the second digital image; wherein the coarse registration mode comprises: generating a first gray-level tissue foreground image from the first digital image and generating a second gray-level tissue foreground image from the second digital image; computing a first tissue binary edge map from the first gray-level tissue foreground image and computing a second tissue binary edge map from the second gray-level tissue foreground image; computing global transformation parameters to align the first binary edge map and the second binary edge map; and, mapping the first digital image and the second digital image to a common grid encompassing both the first and second digital images based on the global transformation parameters; wherein the fine registration mode comprises: annotating the first digital image; mapping the annotation on the common grid to a corresponding location in the second digital image; and, updating the location using Chamfer-distance matching based on the binary tissue edge maps; wherein cropped versions of the tissue edge binary maps are used and the fine registration further comprises selecting a minimum cost window which improves matching relative to the coarse mode registration, wherein the minimum cost window is selected to surround the annotation. a. selecting a first digital image of a first tissue section from a set of digital images of adjacent tissue sections of a single patient; b. selecting a second digital image of a second tissue section from the set; c. matching tissue structure between the first digital image and the second digital image, wherein matching the tissue structure comprises a coarse registration mode, wherein matching the tissue structure further comprises a fine registration mode to refine alignment of the first digital image and the second digital image; and d. automatically mapping an annotation drawn on the first digital image to the second digital image; wherein the coarse registration mode comprises: generating a first gray-level tissue foreground image from the first digital image and generating a second gray-level tissue foreground image from the second digital image; computing a first tissue binary edge map from the first gray-level tissue foreground image and computing a second tissue binary edge map from the second gray-level tissue foreground image; computing global transformation parameters to align the first binary edge map and the second binary edge map; and, mapping the first digital image and the second digital image to a common grid encompassing both the first and second digital images based on the global transformation parameters; wherein the fine registration mode comprises: annotating the first digital image; mapping the annotation on the common grid to a corresponding location in the second digital image; and, updating the location using Chamfer-distance matching based on the binary tissue edge maps; wherein cropped versions of the tissue edge binary maps are used and the fine registration further comprises selecting a minimum cost window which improves matching relative to the coarse mode registration, wherein the minimum cost window is selected to surround the annotation. generating a first gray-level tissue foreground image from the first digital image and generating a second gray-level tissue foreground image from the second digital image; computing a first tissue binary edge map from the first gray-level tissue foreground image and computing a second tissue binary edge map from the second gray-level tissue foreground image; computing global transformation parameters to align the first binary edge map and the second binary edge map; and, mapping the first digital image and the second digital image to a common grid encompassing both the first and second digital images based on the global transformation parameters; wherein the fine registration mode comprises: annotating the first digital image; mapping the annotation on the common grid to a corresponding location in the second digital image; and, updating the location using Chamfer-distance matching based on the binary tissue edge maps; wherein cropped versions of the tissue edge binary maps are used and the fine registration further comprises selecting a minimum cost window which improves matching relative to the coarse mode registration, wherein the minimum cost window is selected to surround the annotation. annotating the first digital image; mapping the annotation on the common grid to a corresponding location in the second digital image; and, updating the location using Chamfer-distance matching based on the binary tissue edge maps; wherein cropped versions of the tissue edge binary maps are used and the fine registration further comprises selecting a minimum cost window which improves matching relative to the coarse mode registration, wherein the minimum cost window is selected to surround the annotation.