Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these 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. The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

中文翻译：

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改进 3D 边缘检测，用于 MRI 配准和对齐的视觉检查

可视化边缘对于神经成像至关重要。例如，边缘图可以保证图像从一种模态（或个体）到另一种模态的自动对齐的质量。我们建议使用二阶导数（高斯差分或 DoG）提供稳健的边缘检测。这种方法是根据大小（这在神经影像学中通常是已知的）而不是强度（这是相对的）来调整。我们证明该方法在广泛的成像模式中表现良好。产生的边缘轮廓一致地形成闭合表面，而替代方法可能会生成断开的线，从而在邻近中引入潜在的模糊性。当前计算边缘的方法基于图像的一阶导数 (FSL) 或 Canny 边缘检测方法 (AFNI) 的变体。这些方法有两个主要限制。首先，这些方法的关键调整参数都与图像强度有关。不幸的是，图像强度对于大多数神经成像模式来说是相对的，使得这些方法的性能不可靠。其次，这些现有方法不一定生成闭合边缘/表面，这会降低确定所表示的边缘与另一图像之间的对应关系的能力。二阶导数非常适合神经影像边缘检测。我们将此方法作为 AFNI 和 FSL 软件包、独立代码和在线的一部分。