Bacterial colony growth characterization using image segmentation with K-means
Abstract
Bacterial characterization is a highly important field within microbiology, which is why it is crucial nowadays that applications focused on this task facilitate and substantially help laboratory technicians who continue to carry out this activity manually, without losing information and considering the diverse characteristics that a bacterial colony possesses. Given this problem, this article proposes the application of the K-means algorithm for the analysis and segmentation of bacterial culture images of Pseudomonas koreensis, with a focus on characterization rather than counting. The purpose of this is to detect the different existing morphologies in the bacterial culture of P. koreensis and know their occupancy percentage in the image. With this information, the aim is to understand the growth and development behavior of the colonies, regardless of whether the user is an expert in bacterial colony detection and characterization or not.
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References
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Copyright (c) 2023 Diana Alejandra Alvarado-Ruiz; Keny Ordaz-Hernández, Grecia Leticia Lara-Cadena, María de Lourdes V. Díaz-Jiménez, Mario Castelán
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
