Cell Counting Algorithm Using Radius Variation, Watershed and Distance Transform


Taehoon Kim, Donggeun Kim, Sangjoon Lee, Journal of Information Processing Systems Vol. 16, No. 1, pp. 113-119, Feb. 2020  

https://doi.org/10.3745/JIPS.04.0158
Keywords: Cell-Counting, Distance Transform, Radius Variation Analysis, watershed algorithm
Fulltext:

Abstract

This study proposed the structure of the cluster's cell counting algorithm for cell analysis. The image required for cell count is taken under a microscope. At present, the cell counting algorithm is reported to have a problem of low accuracy of results due to uneven shape and size clusters. To solve these problems, the proposed algorithm has a feature of calculating the number of cells in a cluster by applying a radius change analysis to the existing distance conversion and watershed algorithm. Later, cell counting algorithms are expected to yield reliable results if applied to the required field.


Statistics
Show / Hide Statistics

Statistics (Past 3 Years)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Kim, T., Kim, D., & Lee, S. (2020). Cell Counting Algorithm Using Radius Variation, Watershed and Distance Transform. Journal of Information Processing Systems, 16(1), 113-119. DOI: 10.3745/JIPS.04.0158.

[IEEE Style]
T. Kim, D. Kim, S. Lee, "Cell Counting Algorithm Using Radius Variation, Watershed and Distance Transform," Journal of Information Processing Systems, vol. 16, no. 1, pp. 113-119, 2020. DOI: 10.3745/JIPS.04.0158.

[ACM Style]
Taehoon Kim, Donggeun Kim, and Sangjoon Lee. 2020. Cell Counting Algorithm Using Radius Variation, Watershed and Distance Transform. Journal of Information Processing Systems, 16, 1, (2020), 113-119. DOI: 10.3745/JIPS.04.0158.