Pulmonary tuberculosis (TB) is one of the top 10 causes of death worldwide caused by an infection. TB is curable with an adequate diagnosis, normally performed through bacilloscopies. Automate TB diagnosis implies bacilli detection and counting usually based on smear images processing and artificial intelligence. Works reported in the literature usually consider images with similar coloring characteristics, which are difficult to obtain due to the Ziehl - Neelsen staining method variations (excess or deficiency of coloration), provoking errors in the bacilli segmentation. This paper presents an image preprocessing technique, based on simple, fast and well-known processing techniques, to improve and standardize the contrast in the Acid-Fast Bacilli (AFB) images used to diagnose TB; these techniques are used previously to the segmentation stage to obtain accurate results. The results are validated with and without the preprocessing stage by the Jaccard index, pixel detection accuracy and UAC obtained in an Artificial Neural Network (ANN) and a Bayesian classifier with Gaussian mixture model (GMM). Obtained results indicate that the proposed approach can be applied to automate the Tuberculosis diagnostic.