Decision Based Adaptive Neighborhood Median Filter

Samantaray, Aswini K.; Mallick, Priyanka

doi:10.1016/j.procs.2015.04.174

Cited by 10 publications

(1 citation statement)

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“…A median filter is a popular noise filter which is used to remove salt and pepper noise in digital images [16][17][18][19] . It has been reported that impulse noise in high-density noisy images can be effectively removed by using an improved algorithm such as a decision based adaptive neighborhood median filter [20]. Mahmood and Mansor [21] used a morphological tool with the Hough transform technique to detect and estimate the number of red blood cells in blood sample images.…”

Section: Introductionmentioning

confidence: 99%

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

Aung

Kanokwiroon

Phairatana

et al. 2019

IJECE

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Cell counting is a required procedure in biomedical experiments and drug testing. Manual cell counting performed with a hemocytometer is time consuming and individual dependence. This study reportedthe development of a computer-assisted program for trypan blue stained-cell counting using digital image analysis. Images of trypan blue-stained breast cancer cells line were obtained by a microscope with a digital camera. Undesired noise and debris were removed by applying a guided image filter. Color space HSV (Hue, Saturation and Value)conversion and grayscale conversion were performed for distinguishing between live and dead cells. Image thresholding and morphological operators were applied for image segmentation. Live and dead cells were counted after image segmentation and the results were compared with manual counting by three well-experienced counters. The computer-assisted cell counting from thirty-six trypan blue-stained microscopic images had a high correlation coefficient with the live cell results of the experts (r=0.99). The correlation coefficient of the number of dead cells comparing the computer-assisted count and the experts’ count was 0.74. Our approach offers high accuracy (>85%)on counting live cells compared with the experts’ counting. This automated cell counting approach can assist biomedical researchers for both live and dead cells counting.

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