###### Abstract
The energy of the device has also greatly increased, leading to improved image quality and larger specimen sizes. In addition, there have been very notable advances in the capacity and speed of postprocessing the information provided by the tomography equipment. However, current technology still has shortcomings in the study of concrete porosity. The first is resolution: although a resolution of 1 \(\upmu\)m is a very small value, it is not sufficient to study mesopores, much less micropores. Moreover, such a high resolution can only be achieved on very small specimens, which limits its use in concrete. The technical challenge for the next decade is to scan specimens of normal size (10-30 cm) at resolutions well below 1 \(\upmu\)m.  
At present, two traditional techniques are used to analyze porosity in concretes, namely nitrogen adsorption and mercury-intrusion porosimetry (MIP) [29; 30; 31; 32]. These techniques can measure really small pore sizes, down to 10 nm or even less, but show important limitations. They can only provide the pore-size distribution, but neither their spatial distribution nor their morphological parameters. Moreover, the information that they provide is related to the open porosity and not to the closed one, which is more interesting when studying this phenomenon. The possibilities for study and research offered by computed tomography are enormously greater than those that traditional techniques can afford, and it is only a matter of time before technology provides science with computed tomography equipment with resolutions of the order of 1 nm. At that time, and actually also right now with the technology available, the next step is to explore what information can be obtained from the large volume of raw data supplied by the tomography equipment. In general terms, the tomography equipment provides a 3D image of the specimen. This image is in fact a set of voxels that are assigned a gray color according to their relative density, so that the voxel corresponding to the lowest density point of the specimen is assigned the color black while the voxel corresponding to the highest density point is assigned the color white. Basically, the file given by the tomography equipment is nothing more than a huge data matrix of millions of rows and only four columns, in which each row contains the X, Y, and Z coordinates of the voxel's center of gravity and a gray number, depending on its average density. In the case of 16-bit images, the gray value 0 corresponds to black (or the least dense point) and the gray value 65,535 corresponds to white (or the densest point). In addition, the file provides some interesting metadata, most notably the voxel size (i.e., the length of the side of the voxel). From this point on, data post-processing begins in order to obtain all the information that the scan can provide. The first step is to identify which voxels correspond to pores and which do not. To do this, a gray threshold must be defined, so that all voxels whose gray level is higher than this threshold are discarded (remember that the darkest voxels are the least dense). The second step is to group all voxels that are in contact, i.e., those whose distance between centers of gravity is under a certain value. Additionally, filters can be applied to separate pores that the software interprets as connected but which are in fact clearly independent entities (e.g., pores connected by a thin neck). One of the best-known techniques is the watershed segmentation [33]. At this stage, all the pores have been identified and isolated. Additionally, everything is ready to start extracting the information that is of real interest. This paper attempts to show some of the most interesting techniques to analyze pores and to extract the maximum information, quantitative and not only qualitative, provided by this technology.