Identification and quantification of pathogenic helminth eggs using a digital image system

AbstractA system was developed to identify and quantify up to seven species of helminth eggs (Ascaris lumbricoides -fertile and unfertile eggs-, Trichuris trichiura, Toxocara canis, Taenia saginata, Hymenolepis nana, Hymenolepis diminuta, and Schistosoma mansoni) in wastewater using different image processing tools and pattern recognition algorithms. The system was developed in three stages. Version one was used to explore the viability of the concept of identifying helminth eggs through an image processing system, while versions 2 and 3 were used to improve its efficiency. The system development was based on the analysis of different properties of helminth eggs in order to discriminate them from other objects in samples processed using the conventional United States Environmental Protection Agency (US EPA) technique to quantify helminth eggs. The system was tested, in its three stages, considering two parameters: specificity (capacity to discriminate between species of helminth eggs and other objects) and sensitivity (capacity to correctly classify and identify the different species of helminth eggs). The final version showed a specificity of 99% while the sensitivity varied between 80 and 90%, depending on the total suspended solids content of the wastewater samples. To achieve such values in samples with total suspended solids (TSS) above 150 mg/L, it is recommended to dilute the concentrated sediment just before taking the images under the microscope. The system allows the helminth eggs most commonly found in wastewater to be reliably and uniformly detected and quantified. In addition, it provides the total number of eggs as well as the individual number by species, and for Ascaris lumbricoides it differentiates whether or not the egg is fertile. The system only requires basically trained technicians to prepare the samples, as for visual identification there is no need for highly trained personnel. The time required to analyze each image is less than a minute. This system could be used in central analytical laboratories providing a remote analysis service