Neurocysticercosis (NCC) is considered a major cause of acquired epilepsy in most developing countries. Humans and pigs acquire cysticercosis ingesting T. solium eggs by the fecal-oral route. After ingestion, oncospheres disperse throughout the body producing cysts mainly in the central nervous system and striated muscles. The treatment is focused on antiparasitic, anti-inflammatory, and antiepileptic drugs; however, new drugs are being studied in animal models recently. The aim of this study was to perform histological image analysis of pig brains with NCC after antiparasitic treatment to develop future tools to study brain inflammation since usually the evaluation of fibrosis is obtained manually on microscopy images in a long, inaccurate, poorly reproducible, and tedious process. For this purpose, the slides of pig brains with NCC were stained with Masson's Trichrome, and high quality photographic images were taken. Then, image processing and machine learning were performed to detect the presence and extension of collagen fibers around the cyst as markers of fibrosis. The process includes the use of color normalization and probabilistic classification implemented in Java language as a plugin to the free access program ImageJ. This paper presents a new method to detect cerebral fibrosis, assessing the amount of fibrosis in the images with accuracy above 75% in 12 seconds. A manual editing tool allows us to raise the results above 90% faster and efficiently.