Hausdorff distance (HD) is commonly used as a similarity metric in image or 3D volume comparison. Although HD is accurate and popular for a variety of tasks, its main weakness is the consumption of processing power. In this work, a novel, parallel and locality-oriented Hausdorff distance algorithm is proposed. Novel as it is the first time in the literature that an algorithmic implementation using morphological dilations is proposed and evaluated for the computation of the Hausdorff distance. Parallel, as it is more robust in terms of parallelization than the state-of-the-art algorithm and local, because it has an intrinsic sense of space and is sensitive to voxels that are spatially closer. This proposal can be faster than the state-of-the-art in several practical cases such as medical imaging registration (up to 8 times faster on average in one of the CPU experiments) and is faster in the worst case (up to 22337 times faster in one of the CPU experiments). Worst-case scenarios and high resolution volumes also favour the proposed approach. Throughout the work, several sequential and parallel CPU and GPU implementations are analyzed and compared.

Hausdorff距离（HD）通常用作图像或3D体积比较中的相似性度量。尽管HD可以准确执行各种任务，但它的主要缺点是处理能力的消耗。在这项工作中，提出了一种新颖的，并行且面向局部的Hausdorff距离算法。这是新颖的，这是文献中首次提出使用形态学扩张的算法实现，并对其进行Hausdorff距离的计算进行了评估。并行，因为它在并行化方面比最新的算法和局部算法更健壮，因为它具有内在的空间感，并且对空间上更近的体素敏感。在一些实际情况下，例如医学成像配准，该建议可以比最新技术快（在一个CPU实验中，平均速度快8倍），在最坏情况下，速度快（最高22337倍）在其中一项CPU实验中速度更快）。最坏的情况和高分辨率的卷也支持该方法。在整个工作中，分析并比较了几种顺序和并行的CPU和GPU实现。