The aerospace and aeronautical industries require extremely demanding manufacturing standards for parts. In this field, Non-Destructive Testing (NDT) plays a fundamental role, as it allows the identification of defects (such as porosity) without the need to damage the parts. Within NDT, computed tomography stands out for its ability to evaluate the internal structure of materials from 2D radiographic images, which are then reconstructed in a 3D model. However, in many cases, the analysis of the tomographic results is carried out manually due to the low contrast between the defects and the material, which makes the process complex, slow and susceptible to human error due to visual fatigue of the operator. This study proposes to optimise such analysis by developing an automated defect detection system using the open-source programming language Python and, specifically, libraries such as OpenCV, NumPy and Pandas to process images and detect contours to quantify, locate and size pores. The system is tested as a first approach with image stacks obtained from two Image Quality Indicators (IQI); standards designed to verify sensitivity in tomography, one Aluminium and one Titanium. The results show that the automated approach improves the efficiency of quality control by reducing analysis time. As future work, we plan to test this methodology on cylindrical specimens with induced porosity and on industrial parts of different geometries