Detection and counting of Leishmania intracellular parasites in microscopy images

TL;DR

This paper presents a computational system for automatically detecting and counting Leishmania parasites in microscope images, improving efficiency and accuracy in drug testing.

Contribution

A novel image processing system for automatic detection and counting of Leishmania amastigotes in microscopy images is developed.

Findings

The system uses multilevel Otsu segmentation and watershed transform for object separation.

Classifier algorithms were evaluated for accurate amastigote detection.

The proposed method achieved high effectiveness with favorable sensitivity and precision metrics.

Abstract

Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania and has 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 in which a technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is slow, laborious, and prone to errors.

To develop a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques.

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 hue, saturation, and intensity color model. In addition, morphological operations and the watershed transform combined with the weighted external distance transform were used to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, the selection of which 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.

The results were evaluated in terms of sensitivity, precision, and the F-measure and suggested a favorable effectiveness of the proposed method.

This system can help researchers by allowing large volumes of images of amastigotes to be counted using an automatic image analysis technique.

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