Live and Dead Cells Counting from Microscopic Trypan Blue Staining Images using Thresholding and Morphological Operation Techniques

Aung, Su Mon; Kanokwiroon, Kanyanatt; Phairatana, Tonghathai; Chatpun, Surapong

doi:10.11591/ijece.v9i4.pp2460-2468

Cited by 7 publications

(9 citation statements)

References 27 publications

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“…The learning phase includes the following steps.  Step 1: Grayscale image conversion using a thresholding algorithm [23], [24]. The result of the grayscale image conversion is dependent on threshold values of image intensity as shown in Figure 6.…”

Section: Learning Phasementioning

confidence: 99%

Image-based lime size grading using the comparison ratio of the pixel radius and the actual size of lime fruit

Chimlek

Jitanan²

2021

IJEECS

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Lime is a commercially important fruit in Thailand whose sale price depends on the fruit’s size; hence, farmers must grade limes by size before distribution. However, as lime grading machines are very expensive and each province has different size grading limits, grading is often performed manually, which is time-consuming and error-prone. Agricultural production systems for automatic selection and grading use image processing techniques for extracting key features. Therefore, this study proposes techniques to extract features of limes and to develop analytical methods for grading them. This method can reduce time and cost, and increase accuracy and flexibility for selecting different lime sizes according to each province’s size criteria. To verify our method, we classified limes according to criteria from four Thailand provinces as sample data in an experiment. The focal image feature was the radius or diameter of the lime and the grading conditions were defined by the maximum comparison ratio of the fruit’s radius in pixels to the measured radius of the actual lime in centimeters. The average grading accuracy was 99.59%, which outperformed that of mechanical grading. The processing time was 1.70 seconds per individual fruit.

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Section: Learning Phasementioning

confidence: 99%

Image-based lime size grading using the comparison ratio of the pixel radius and the actual size of lime fruit

Chimlek

Jitanan²

2021

IJEECS

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“…ImageJ). Firstly, a good ML model makes manual image (pre-)processing obsolete [ 15 , 17 ]. Secondly, enabling batch image processing could reduce significantly overall process time in the evaluation of multiple cultures (e.g.…”

Section: Introductionmentioning

confidence: 99%

Automated cell counting for Trypan blue-stained cell cultures using machine learning

Kuijpers,

van Veen,

van der Pol

et al. 2023

PLoS ONE

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Cell counting is a vital practice in the maintenance and manipulation of cell cultures. It is a crucial aspect of assessing cell viability and determining proliferation rates, which are integral to maintaining the health and functionality of a culture. Additionally, it is critical for establishing the time of infection in bioreactors and monitoring cell culture response to targeted infection over time. However, when cell counting is performed manually, the time involved can become substantial, particularly when multiple cultures need to be handled in parallel. Automated cell counters, which enable significant time reduction, are commercially available but remain relatively expensive. Here, we present a machine learning (ML) model based on YOLOv4 that is able to perform cell counts with a high accuracy (>95%) for Trypan blue-stained insect cells. Images of two distinctly different cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and Spodoptera frugiperda (Sf9), were used for training, validation, and testing of the model. The ML model yielded F1 scores of 0.97 and 0.96 for alive and dead cells, respectively, which represents a substantially improved performance over that of other cell counters. Furthermore, the ML model is versatile, as an F1 score of 0.96 was also obtained on images of Trypan blue-stained human embryonic kidney (HEK) cells that the model had not been trained on. Our implementation of the ML model comes with a straightforward user interface and can image in batches, which makes it highly suitable for the evaluation of multiple parallel cultures (e.g. in Design of Experiments). Overall, this approach for accurate classification of cells provides a fast, bias-free alternative to manual counting.

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“…Many researchers used these morphology operations to get image segments in different applications such as in medicine and recognition. Aung et al [13] using trypan blue-stained microscopic pictures of a breast cancer cell line, a computer-assisted cell counting technique was presented, in which live and dead cells were counted following image segmentation. A guided image filter was used to eliminate unwanted noise and clutter.…”

Section: Introductionmentioning

confidence: 99%

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Ibrahim

Al-Saleh

Jabar

2023

IJEECS

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<p>Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.</p>

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Live and Dead Cells Counting from Microscopic Trypan Blue Staining Images using Thresholding and Morphological Operation Techniques

Cited by 7 publications

References 27 publications

Image-based lime size grading using the comparison ratio of the pixel radius and the actual size of lime fruit

Image-based lime size grading using the comparison ratio of the pixel radius and the actual size of lime fruit

Automated cell counting for Trypan blue-stained cell cultures using machine learning

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

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