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A new approach for the study of geometric morphometrics is presented based on well-established image processing techniques in a novel combination to support high-throughput analysis necessary for large-scale determination of genotype-phenotype relationships. The method retains full 3-D data, and avoids manual landmark selection. Micro-computed tomography images are superimposed into a common orientation by rigid image registration with an isotropic scale factor. An average sample shape is determined by averaging the intensities of corresponding voxels of the registered images, and shape variation is determined by calculating the image gradient of the average shape. Localized shape differences between mean images or between an individual and a group mean are identified and quantified by surface-to-surface distance measures of superimposed images. Validation was performed using geometric shapes of known dimensions as well as biological samples of C57 BL/6 J and A/WySnJ mouse skulls, and shape variation of the mouse skulls was consistent with previously published results. Although the image gradient is sensitive to both image registration and filtration of the average image, the effect can be minimized by consistent use of image analysis parameters. While the proposed approach deviates from well-established landmark-based geometric morphometric tools, it is not intended to replace these current methods. Rather, it will be an important contribution to provide high-throughput screening in large-scale studies focused on understanding genotype-phenotype relationships so that subsequent morphometric approaches using established techniques can be better focused.