AUTHOR=Portuondo-Mallet Lariza María de la Caridad , Mollineda-Diogo Niurka , Orozco-Morales Rubén , Lorenzo-Ginori Juan Valentín 

TITLE=Detection and counting of Leishmania intracellular parasites in microscopy images

JOURNAL=Frontiers in Medical Technology

VOLUME=Volume 6 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/medical-technology/articles/10.3389/fmedt.2024.1360280

DOI=10.3389/fmedt.2024.1360280

ISSN=2673-3129

ABSTRACT=Problem: Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania with a high prevalence and impact on global health. Currently, the available drugs for its treatment have drawbacks such as high toxicity, resistance of the parasite and high cost. Therefore, the search for new, more effective and safe drugs is a priority. The effectiveness of an anti-leishmanial drug is analyzed through in vitro studies where the technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is a slow, laborious and prone to errors process. Objective(s): Development of a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques. Methodology: Segmentation of objects in the microscope image that might be Leishmania amastigotes was performed using the multilevel Otsu method on the Saturation component of the HSI color model, as well as morphological operations and the watershed transform combined with the weighted external distance transform in order to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, whose selection as well as the definition of the features to be used were also part of this research. Matlab was used for the development of the system. Results and discussion: The results were evaluated in terms of sensitivity, precision and F-measure and suggest a favorable effectiveness of the proposed method. Conclusions: This system can help the researcher by allowing the process of counting amastigotes in large volumes of images through an automatic image analysis technique.