Studying porous rock materials with X-Ray Computed Tomography (XRCT) has been established as a standard procedure for the non-destructive visualization of flow and transport in opaque porous media. Despite the recent advances in the field of XRCT, some challenges still remain due to the inherent noise and imaging artefacts in the produced data. These issues become even more profound when the objective is the identification of fractures, and/or fracture networks. The challenge is the limited contrast between the regions of interest and the neighboring areas. This limited contrast can mostly be attributed to the minute aperture of the fractures. In order to overcome this challenge, it has been a common approach to apply digital image processing, such as filtering, to enhance the signal-to-noise ratio. Additionally, segmentation methods based on threshold-/morphology schemes can be employed to obtain enhanced information from the features of interest. However, this workflow needs a skillful operator to fine-tune its input parameters, and the required computation time significantly increases due to the complexity of the available methods, and the large volume of the data-set. In this study, based on a data-set produced by the successful visualization of a fracture network in Carrara marble with XRCT, we present the segmentation results from a number of segmentation methods. Three conventional and two machine-learning-based methods are evaluated. The segmentation results from all five methods are compared to each other in terms of segmentation quality and time efficiency. Due to memory limitations, and in order to accomplish a fair comparison, all the methods are employed in a 2D scheme. The output of the 2D U-net model, which is one of the adopted machine-learning-based segmentation methods, shows the best performance regarding the quality of segmentation and the required processing time.

翻译：研究透漏的岩石材料时使用 X- Ray X- Ray Compute Tomagraphy(XRCT) 是一个标准程序,用于在不透明、不透明、不透明媒体中对流和运输进行非破坏性的可视化。尽管在XRCT领域最近取得了进步,但由于生成的数据中固有的噪音和成像手工艺,因此仍然存在一些挑战。当目标是识别断裂和(或)断裂网络时,这些问题就变得更加深刻。挑战在于有关区域和周边区域之间的对比有限。这种有限的对比主要可归因于骨折的微小孔。为了克服这一挑战,采用数字图像处理(例如过滤)的标准程序是一种常见的比较方法,例如采用数字图像处理(例如过滤),以加强信号对音异比比比率。此外,基于门槛/形态方法的分解方法可以用来从感兴趣的特性获得更好的信息。然而,这种工作流程需要熟练的操作者对其输入参数进行微调,而由于现有方法的复杂性和数据集成体的庞大数量,因此,为了克服这个研究,在进行数字- 将数据质量处理过程中,根据两个分解的系统生成的分流的分解方法,我们通过正常分解的分流的分解的分解的分解方法,所有的分解方法显示的分解结果,从所有的分解的分解方法显示的分解,从所有的分解的分解的分解,从所有分解方法从所有的分解方法从所有分算方法从所有的分解方法从所有输入的分算方法从所有的分。