We describe a computational method to assist radiologists in performing better, more reliable and simpler diagnosis of neurocysticercosis (NC). Based on this method we implemented a software system that counts and measures the calcifications related to NC in computed tomography (CT) scans, thus reducing errors regarding visual inspection and providing better quantitative data. During computation, the system segments grey scale images obtained by CT scans and resulting segments are submitted to classification using artificial neural networks (ANNs). The system marks NC findings, replacing automatically all areas in the original image classified as NC with specially coloured markings. Afterwards, the system starts correlating NC-findings in differents slices and performing a 3D reconstruction based on NC-classified areas belonging to the same finding. As a final step, the system performs a 3D reconstruction of the patient's skull, encephalic mass and findings boundaries, generating a 3D representation of the patient's head and the localisation of NC findings. In this step the volumes of each NC finding are also calculated.