Vascular structure enhancement is very useful in image processing and computer vision. The enhancement of the presence of the structures like tubular networks in given images can improve image-dependent diagnostics and can also facilitate tasks like segmentation. The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in 3D images due to the extremely complicated orientation in 3D against 2D. Given the rising demand and interest in handling 3D images, we experiment with modularising the concept and generalise the algorithm to 3D curves. In this work, we propose a 3D orientation field transform. It is a vascular structure enhancement algorithm that can cleanly enhance images having very low signal-to-noise ratio, and push the limits of 3D image quality that can be enhanced computationally. This work also utilises the benefits of modularity and offers several combinative options that each yield moderately better enhancement results in different scenarios. In principle, the proposed 3D orientation field transform can naturally tackle any number of dimensions. As a special case, it is also ideal for 2D images, owning a simpler methodology compared to the previous 2D orientation field transform. The concise structure of the proposed 3D orientation field transform also allows it to be mixed with other enhancement algorithms, and as a preliminary filter to other tasks like segmentation and detection. The effectiveness of the proposed method is demonstrated with synthetic 3D images and real-world transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones. Extensive experiments and comparisons with existing related methods also demonstrate the excellent performance of the proposed 3D orientation field transform.

血管结构增强在图像处理和计算机视觉中非常有用。给定图像中管状网络等结构的存在的增强可以改善图像相关的诊断，并且还可以促进分割等任务。二维 (2D) 方向场变换已被证明可以通过自上而下的处理有效增强图像中的 2D 轮廓和曲线。然而，由于 3D 相对于 2D 的方向极其复杂，因此它在 3D 图像中没有对应物。鉴于处理 3D 图像的需求和兴趣不断增长，我们尝试模块化概念并将算法推广到 3D 曲线。在这项工作中，我们提出了 3D 方向场变换。它是一种血管结构增强算法，可以清晰地增强信噪比极低的图像，并突破可通过计算增强的 3D 图像质量的极限。这项工作还利用了模块化的优势，并提供了几种组合选项，每种选项在不同的场景中都会产生更好的增强结果。原则上，所提出的 3D 方向场变换可以自然地处理任意数量的维度。作为一种特殊情况，它也非常适合 2D 图像，与之前的 2D 方向场变换相比，它具有更简单的方法。所提出的 3D 方向场变换的简洁结构还允许其与其他增强算法混合，并作为分割和检测等其他任务的初步过滤器。该方法的有效性通过合成 3D 图像和真实世界的透射电子显微镜断层图（从 2D 曲线增强到更重要和有趣的 3D 曲线）得到了证明。大量的实验以及与现有相关方法的比较也证明了所提出的 3D 方向场变换的优异性能。