A Review of Clustering Methods in Microorganism Image Analysis

Li, Chen; Kulwa, Frank; Jing-hua, Zhang; Xu, Hao; Zhao, Xin

doi:10.1007/978-3-030-49666-1_2

Cited by 32 publications

(21 citation statements)

References 28 publications

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“…In summary, k-means has achieved results on how to select the initial center K value and reduce the number of iterations [25][26][27][28][29][30]. However, due to the blindness of the initial center K value selection, the cluster number needs to be determined in advance, and there are problems such as local optimization.…”

Section: Related Technologymentioning

confidence: 99%

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Yan

et al. 2021

Complexity

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At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence.

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Section: Related Technologymentioning

confidence: 99%

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Yan

et al. 2021

Complexity

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“…Microorganisms are tiny living organisms which can appear as unicellular, multicellular, and acellular [1]. Some are advantageous, and some are harmful to human health and environments.…”

Section: Introductionmentioning

confidence: 99%

A new pairwise deep learning feature for environmental microorganism image analysis

Kulwa

Zhang

et al. 2022

Environ Sci Pollut Res

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Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points' locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs

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“…The main methods to remove noise are wire filter (such as Gaussian filter and Mean filter), median filter and so on (Li et al. 2020b ).…”

Section: Introductionmentioning

confidence: 99%

“…The next step is microorganism counting (Li et al. 2020b ). The objects of the microorganism counting method are separating the adherent colonies and counting.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

et al. 2021

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Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

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A Review of Clustering Methods in Microorganism Image Analysis

Cited by 32 publications

References 28 publications

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

A new pairwise deep learning feature for environmental microorganism image analysis

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

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