To analyse multimodal 3-dimensional medical images, interpolation is required for resampling which - unavoidably - introduces an interpolation error. In this work we consider three segmented 3-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.

翻译：为了分析多式三维医疗图像,必须进行内插才能重新取样,这不可避免地会引入一个内插错误。在这项工作中,我们认为三个分立的三维图像与三个不同的神经成像软件工具进行了重新取样,以比较低抽样和过度抽样战略,并查明过度抽样错误的所在位置。结果显示,低抽样到最低图像大小在每段误差的平均值方面是有利的,在梯度更陡峭的地方,过度抽样错误更大,显示出Gibbs效应。