The use of image analysis for spore counts of white-rot fungi

Jones, Cameron L.; Lonergan, Greg T.; Mainwaring, David E.

doi:10.1007/bf02447481

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…It only considers those connections that minimize the total length across the graph (Figure 1C; Prim, 1957). MST has a long history of application in biology and ecology to describe various spatial patterns (Dussert et al, 1986(Dussert et al, , 1987Cantwell and Forman, 1993;Lockwood et al, 1993;Jones et al, 1996;Keitt et al, 1997;Wallet and Dussert, 1997;Bunn et al, 2000, Urban andKeitt, 2001;Labra et al, 2005). However, to fully describe the invasion process, the MST should be rooted at the first recorded invasion site and directed to reflect the temporal sequence of site invasion.…”

Section: Introductionmentioning

confidence: 99%

Disentangling the spread dynamics of insect invasions using spatial networks

et al. 2023

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IntroductionDescribing and understanding spatiotemporal spread patterns in invasive species remains a long-standing interdisciplinary research goal. Here we show how a network-based top-down approach allows the efficient description of the ongoing invasion by Drosophila suzukii in Chile.MethodsTo do so, we apply theoretical graph methods to calculate the minimum cost arborescence graph (MCA) to reconstruct and understand the invasion dynamics of D. suzukii since the first detection in 2017. This method estimates a directed rooted weighted graph by minimizing the total length of the resulting graph. To describe the temporal pattern of spread, we estimate three metrics of spread: the median dispersal rate, the median coefficient of diffusion, and the median dispersal acceleration.ResultsThe estimated MCA shows that over four years, D. suzukii colonized a ~1,000km long strip in the central valley of Chile, with an initial phase with long paths and connections and no clear direction pattern, followed by a clearer north–east propagation pattern. The median dispersal rate for the entire period was 8.8 (7.4–10.6, 95% CI), while the median diffusion coefficient was 19.6 meters2/day (13.6–27.9, 95% CI). The observed spread dynamics and the log-normal distribution of accelerations are consistent with long-distance dispersal events.DiscussionThe complexities of real landscapes cannot be summarized in any model, but this study shows how an alternative top-down approach based on graph theory can facilitate the ecological analysis of the spread of an invasive species in a new territory.

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Section: Introductionmentioning

confidence: 99%

Disentangling the spread dynamics of insect invasions using spatial networks

et al. 2023

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“…In Jones et al. ( 1992 ), local threshold and morphological transformation are used to calculate spore numbers accurately. Red and blue signals are sampled for pixel segmentation.…”

Section: Other Microorganism Counting Methodsmentioning

confidence: 99%

“…( 1995 ) Heterotrophic bacteria Edge strength filter, minimum and maximum cell size filter and Marr-Hildeth method Jones et al. ( 1992 ) Spores RGB processing Local thresholding and morphological transform Zalewski and Buchholz ( 1996 ) Yeast cells Color filtering and contour enhancement Thresholding Shabtai et al. ( 1996 ) Fungi Self-organizing multilayer neural network Akiba and Kakui ( 1997 ) Plankton Local auto-correlational masks PCA Auto-correlational masks and discrimination analysis 90% accuracy Robinson et al.…”

Section: Other Microorganism Counting Methodsmentioning

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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“…Billon et al [185] have monitored the germination of single spores of Clostridium botulinum by observing them with a phase-contrast microscope and simply recording the change in refractivity. Jones et al [186] have observed also a change in refractivity when they developed their automated method for Phanerochaete chrysosporium, but they have suggested it may be due to water evaporation. Recombinant bacteria such as E. coli can store the foreign proteins they produce as inclusion bodies [53,187] or crystals [188] which do not refract light as the rest of the cell.…”

Section: ) Pellet Convex Area -Core Convex Areamentioning

confidence: 97%

Biomass Quantification by Image Analysis

Pons

Vivier

1999

Bioanalysis and Biosensors for Bioprocess Monitoring

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Microbiologists have always rely on microscopy to examine microorganisms. When microscopy, either optical or electron-based, is coupled to quantitative image analysis, the spectrum of potential applications is widened: counting, sizing, shape characterization, physiology assessment, analysis of visual texture, motility studies are now easily available for obtaining information on biomass. In this chapter the main tools used for cell visualization as well as the basic steps of image treatment are presented. General shape descriptors can be used to characterize the cell morphology, but special descriptors have been defined for filamentous microorganisms. Physiology assessment is often based on the use of fluorescent dyes. The quantitative analysis of visual texture is still limited in bioengineering but the characterization of the surface of microbial colonies may open new prospects, especially for cultures on solid substrates. In many occasions, the number of parameters extracted from images is so large that data-mining tools, such as Principal Components Analysis, are useful for summarizing the key pieces of information.

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The use of image analysis for spore counts of white-rot fungi

Cited by 12 publications

References 14 publications

Disentangling the spread dynamics of insect invasions using spatial networks

Disentangling the spread dynamics of insect invasions using spatial networks

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

Biomass Quantification by Image Analysis

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