Pattern Recognition Algorithm for Automatic Quantification of Toxoplasma gondii Tachyzoites

Abstract

Context: Digital image processing is an efficient and suitable computational tool for the automatic quantification of human pathogens in images, providing analysis in less time, greater number of samples, and result reproducibility. We propose the development and validation of an image processing algorithm, for the recognition and automatic quantification of T. gondii tachyzoites. Method: We developed an algorithm based on image processing. This workflow allows identifying the morphology of each parasite in the image by determining the number of parasites distinguishing them from those with a similar morphology, but not corresponding to the parasite in question. Images were obtained through Giemsa staining protocols. Results: The original images were analyzed by experts. The results showed correlation with those obtained by the automatic count. Additionally, a processing time of 5 seconds per image was obtained with the algorithm. This automated quantification tool allowed count of tachyzoites in tens of images. Conclusions: This automatic image analysis tool can extend its implementation to any laboratory that is involved in the quantification of extracellular Toxoplasma gondii tachyzoites, as well as other aspects of research on its tachyzoites that require the count of this form of development of the parasite.