Use of Imagej, Smartphone Camera, and Light Microscope to Evaluate Stallion Sperm Concentration

Freitag, G.; Lima, Luis Gustavo Freitag de; Kozicki, Luiz Ernandes; Felicio, Liedge Camila Simioni; Weiss, Romildo Romualdo

doi:10.5380/avs.v25i3.71839

Cited by 3 publications

(4 citation statements)

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“…ANOVAs revealed relevant differences among the three methods of motility estimation, with the ISAS ® v1 presenting greater values compared with iSperm ® and Open CASA v2, the latter showing the lowest values for total and progressive motility. Earlier studies also revealed similar differences in other species (stallion [20,28,42]; fish [22]) with these systems. In this case, variation could be due to the temperature of analysis [10,48], presence of agglutination [49], or composition of the ejaculates and their heterogeneity, and can be explained by the existence of different cell groupings or subpopulations within ejaculate [50,51].…”

Section: Discussionsupporting

confidence: 67%

“…Our results showed that sperm motility and concentration estimates have relevant differences between methods. These could be explained by the method of estimation [39], calibrations of systems [3,10], settings of systems and algorithms [40,41], the luminous capacity of systems [42], or the type of chamber used for analysis [43][44][45][46][47]. Male differences could also be a source of variation.…”

Section: Discussionmentioning

confidence: 99%

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Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Sevilla,

Soler,

Araya-Zúñiga

et al. 2023

Animals

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Artificial insemination in the swine industry, as in other species, demands adequate semen handling and accurate evaluation for the preparation of seminal doses. Sperm concentration and motility estimates are part of the semen evaluation process and are considered important for maximizing the yield of doses for insemination. In this study, methods were examined for their accuracy in the estimation of boar sperm concentration and motility. Assessments of sperm concentration were carried out using iSperm®, ISAS® v1, Open CASA v2, and the Accuread® photometer. Analyses of sperm motility were performed with iSperm®, ISAS® v1, and Open CASA v2 systems. In this study, boar semen samples were collected from 10 healthy males from two genetic lines. There were no relevant differences between sire lines when sperm concentration was assessed. A Bayesian analysis was applied to the four methods used to assess sperm concentration to examine whether there are relevant differences between them. Results suggested differences in the four methods, with a probability of relevance (PR) of 0.86–1.00. The iSperm® method revealed higher concentration values within the highest posterior density region at 95% confidence interval (HPD95%) = 167.0, 224.2 M/mL, whereas Open CASA v2 showed the lowest values, with HPD95% = 99.3, 155.9 M/mL. The iSperm® demonstrated higher reliability in measuring sperm concentration compared to other methods or devices within the given range of confidence. ANOVAs revealed relevant differences in the three methods of motility estimation. Overall, differences in boar sperm concentration and motility estimates were found using various methods, but further studies are needed for better characterization of these differences.

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Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Sevilla,

Soler,

Araya-Zúñiga

et al. 2023

Animals

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“…The configurations and its modifications is provided in Table 1. Refer to the laboratory experiment results, several factors can affect measurement results such as ambient light interference, light source intensity, the distance between camera and sample cell, the angel of light source, the angel of camera, as well as dark background implementation [18]. In measuring turbidity with the enumeration approach of suspended particles, the number of particles and the calculated surface area are then substituted into the linear equation as a result of the measurement of the number of particles and the measurement results with a commercial turbidimeter against the standard series of p-ISSN : 2528-3561 e-ISSN : formazine suspensions.…”

Section: Measurement-chamber Configurationsmentioning

confidence: 99%

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Wikaningrum¹,

Alvasa²,

Panelin³

et al. 2021

JSE

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Monitoring of pollutant concentrations in surface water becomes a concern, considering the utilization of surface water as the raw water for drinking water treatment plants (WTP). The fluctuation of pollutant concentrations in surface water can affect the performance of WTP. This research was conducted to assess the potential for turbidity level prediction based on the calculation of the number and surface area of suspended particles through a digital image processing approach. Measurements of the amount and surface area were carried out in the form of laboratory-scale experiments using the open source software ImageJ 1.46r. The algorithm in ImageJ can convert pixels into a number “value” and surface area through a series of digital image processing steps, henceforth compared with the existing measurement method. The results showed that there was a strong correlation between the number of particles and the concentration of formazine suspension (r = 0.9821), but does not apply to the surface area. Referring to the results of laboratory experiments, it can be concluded that the approach to measure the number of suspended particles can be the basis for predicting the turbidity level in the turbidity range 100-800 NTU, but does not apply to the turbidity range 0.02-20 NTU.

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“…ImageJ is a publicly available image processing software based on Java that was developed by the National Institutes of Health [13,14]. This software is frequently used to analyze medical problems, such as contaminated areas [15], sperm density [16], fluorescent cell stains [4], and corneal neovascularization [17]. However, the software is used by first taking a photo with a mobile phone or camera, and then analyzing the photo using ImageJ software on a computer.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Contaminated Areas Using Ultraviolet Fluorescence Markers for Medical Simulation: A Mobile Phone Application Approach

et al. 2023

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The use of ultraviolet fluorescence markers in medical simulations has become popular in recent years, especially during the COVID-19 pandemic. Healthcare workers use ultraviolet fluorescence markers to replace pathogens or secretions, and then calculate the regions of contamination. Health providers can use bioimage processing software to calculate the area and quantity of fluorescent dyes. However, traditional image processing software has its limitations and lacks real-time capabilities, making it more suitable for laboratory use than for clinical settings. In this study, mobile phones were used to measure areas contaminated during medical treatment. During the research process, a mobile phone camera was used to photograph the contaminated regions at an orthogonal angle. The fluorescence marker-contaminated area and photographed image area were proportionally related. The areas of contaminated regions can be calculated using this relationship. We used Android Studio software to write a mobile application to convert photos and recreate the true contaminated area. In this application, color photographs are converted into grayscale, and then into black and white binary photographs using binarization. After this process, the fluorescence-contaminated area is calculated easily. The results of our study showed that within a limited distance (50–100 cm) and with controlled ambient light, the error in the calculated contamination area was 6%. This study provides a low-cost, easy, and ready-to-use tool for healthcare workers to estimate the area of fluorescent dye regions during medical simulations. This tool can promote medical education and training on infectious disease preparation.

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Use of Imagej, Smartphone Camera, and Light Microscope to Evaluate Stallion Sperm Concentration

Cited by 3 publications

References 0 publications

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach

Prediction of Contaminated Areas Using Ultraviolet Fluorescence Markers for Medical Simulation: A Mobile Phone Application Approach

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