Microscopic Image-Based Ascaris Egg Detection using features extracting Approach

Abstract

Ascaris suum is a significant zoonotic parasite affecting swine. Its impact extends beyond swine health, as it also leads to slaughterhouse condemnations. These parasitic diseases are responsible for substantial economic losses in the pig production industry. Monitoring the parasite load through stool ova and parasite analysis to minimize losses to the producer. Possible zoonotic potential of Ascaris spp. has been observed by several studies across the globe. Their effective transmission a major concern for both human and animal health as they can be transmitted through soil, water and contaminated food. Ascaris suum is a large roundworm of pigs which is one of the economically most disruptive pig parasites worldwide. Ascarid eggs are easy to transmit, yet harder to detect with even reliable detection in environmental media posing as a significant challenge. In recent years, however, the issue of environmental contamination with ascarid ova has garnered increased attention as a critical component of comprehensive risk assessment in swine production. Various factors, including sample matrices and flotation solutions, complicate the establishment of defined and standardized protocols for isolating eggs from complex environmental matrices, resulting in limitations. Microscopic image-based Ascaris eggs detection offers a promising technique to improve diagnostic accuracy and efficiency. In this paper, high-resolution microscopic images of pig fecal samples have been captured through mobile phone attached with microscope. The eggs from captured images have been segmented and counted through sequence of image processing steps. Three approaches have been applied on captured images and the best approach has been counted the eggs with 98% accuracy found outperform compared to previous detection accuracies. This paper shows that the microscopic image-based Ascaris egg detection has the potential to revolutionize parasitic disease diagnostics, making it faster, more reliable, and accessible to a wider population and it also provides a review of existing techniques for the extraction of ascarid eggs from environmental samples, with a particular emphasis on sampling strategies and methodologies.