Improving 3D Edge Detection for Visual Inspection of MRI Coregistration and Alignment

Abstract

Detecting and visualizing edges is important in several neuroimaging and medical imaging applications. For example, it is common to use edge maps to ensure the automatic alignment of low-resolution functional MRI images to match a high-resolution structural image has been successful. Specifically, software toolboxes like FSL and AFNI generate volumetric edge maps that can be particularly useful for visually assessing the alignment of datasets, overlaying the edge map of one on the other. Therefore, edge maps play a crucial role in quality assurance. Popular methods for computing edges are based on either the first derivative of the image as in FSL, or a variation of the Canny Edge detection method as implemented in AFNI. The crucial algorithmic parameter for adjustment for each of these methods relates to the image intensity. However, image intensity is relative and can be quite variable in most neuroimaging modalities. Further, the existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. We suggest that using the second derivative (difference of Gaussian, or DoG) of the image to generate edges resolves both these issues. This method primarily operates by specifying a spatial scale of interest (which is typically known in medical imaging) rather than a contrast scale, and creates closed surfaces by definition. We describe some convenient implementation features (for both efficiency and visual quality) developed here, and we provide open source implementations of this method as both online and high performance portable code. Finally, we include this method as part of both the AFNI and FSL software packages.