Identification and quantification of pathogenic helminth eggs using a digital image system
Por:
Jimenez, B., Maya, C., Velasquez, G., Torner, F., Arambula, E., Barrios, J. A., Velasco, M.
Publicada:
1 jul 2016
Resumen:
A 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. (C)
2016 The Authors. Published by Elsevier Inc.
Filiaciones:
Jimenez, B.:
Univ Nacl Autonoma Mexico, Inst Ingn, POB 70-186, Mexico City 04510, DF, Mexico
Maya, C.:
Univ Nacl Autonoma Mexico, Inst Ingn, POB 70-186, Mexico City 04510, DF, Mexico
Velasquez, G.:
Univ Nacl Autonoma Mexico, Ctr Ciencias Aplicadas & Desarrollo Tecnol, POB 70-186, Mexico City 04510, DF, Mexico
Torner, F.:
Univ Nacl Autonoma Mexico, Inst Ingn, POB 70-186, Mexico City 04510, DF, Mexico
Arambula, E.:
Univ Nacl Autonoma Mexico, Ctr Ciencias Aplicadas & Desarrollo Tecnol, POB 70-186, Mexico City 04510, DF, Mexico
Barrios, J. A.:
Univ Nacl Autonoma Mexico, Inst Ingn, POB 70-186, Mexico City 04510, DF, Mexico
Velasco, M.:
Univ Nacl Autonoma Mexico, Inst Ingn, POB 70-186, Mexico City 04510, DF, Mexico
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