Morphological characterization of filamentous microorganisms in submerged cultures by on-line digital image analysis and pattern recognition

Treskatis, S.-K.; Orgeldinger, V.; Wolf, H.; Gilles, Ernst Dieter

doi:10.1002/(sici)1097-0290(19970120)53:2<191::aid-bit9>3.0.co;2-j

Cited by 79 publications

(40 citation statements)

References 17 publications

(17 reference statements)

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“…These authors observed that the mycelial growth of these microorganisms is complex and that computer vision systems can be useful to model mycelial growth. Treskatis et al (1997) developed an algorithm for the morphological characterization and estimation of biomass of filamentous microorganisms by means of digital image analysis that allowed continuous monitoring of the fermentation process. Couri et al (2006) developed a computer vision system that measured the area occupied by the hyphae of fungi and correlated it with the total biomass produced.…”

Section: Resultsmentioning

confidence: 99%

Mycelial growth assessment by digital image analysis in R software environment

Oliveira

Itako

Júnior

2017

Idesia

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This study was aimed at developing a script in R to assess fungal growth in Petri dishes using computer vision. The script was developed to aid studies in agricultural phytopathology. The fungi used were Elsinoe ampelina, Fusarium oxysporum and Fusarium verticillioides. The images were analyzed with R and the EBImage package. The command computeFeatures.shape was used to calculate the area and the mean diameter of the colony and the label in square pixels. The script was run in a loop to automate the analysis of all images in sequence. The script developed in R with the package EBImage was able to recognize and measure the fungus colonies examined. Simultaneously with photographing, the mean diameter of colonies was obtained by calculating the arithmetic mean of the diameter measured with two straight perpendicular lines using a ruler. Using the mean diameter, the area of the colony was calculated as a circle. The coefficient of determination and the root mean square error were calculated using the measurements and the values obtained with the program. Based on the coefficient of determination, the measurements obtained with the software are similar to those made using a ruler. The script reduces the image analysis time and increases accuracy, especially for non-uniform colonies.Key words: software, computer vision, EBImage, mycelial growth. RESUMEN Este estudio tuvo como objetivo el desarrollo de un script en R para evaluar el crecimiento de hongos en placas de Petri utilizando la visión por ordenador. El script fue desarrollado para ayudar a los estudios de fitopatología agrícola. Los hongos utilizados fueron

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Section: Resultsmentioning

confidence: 99%

Mycelial growth assessment by digital image analysis in R software environment

Oliveira

Itako

Júnior

2017

Idesia

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“…In this case, spectroscopy is may be able to classify the invariable structure of yeast, the metabolism and cell communication. Cells morphology is a visible expression of microorganisms physiology and metabolism (Treskatis et al, 1997). Different morphologies characterize different proteomic composition and perhaps different metabolism states that can be differentiated by UV-VIS-SWNIR spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Yeast Metabolic State Identification by Fiber Optics Spectroscopy

Castro¹,

Silva²,

Lopes³

et al. 2009

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:In this manuscript we explore the feasibility of using LWUV-VIS-SWNIR (340 -1100 nm) spectroscopy to classify Saccharomyces cerevisiae colony structures in YP agar and YPD agar, under different growth conditions, such as:Results show that LWUV-VIS-SWNIR spectroscopy has the potential for yeasts metabolic state identification once the spectral signatures of colonies differs from each others, being possible to acheive 100% of classification in UV-VIS and VIS-SWNIR. The UV-VIS region present high discriminant information (350-450 nm), and different responses to UV excitation were obtained. Therefore, high precision is obtained because UV-VIS and VIS-NIR exhibit different kinds of information. In the future, high precision analytical chemistry techniques such as mass spectroscopy and molecular biology transcriptomic studies should be performed in order to understand the detailed cell metabolism and genomic phenomena that characterize the yeast colony state.

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“…Densitometric techniques (grey-scale analysis of a given image) were used successfully by Treskatis et al (1997) for biomass quantification of a submerged culture of the filamentous bacterium Streptomyces tendae, in an attempt to accompany the fermentative process on line. On the other hand, the new mathematical wavelet transform technique in its computer-based version is attracting the attention of specialists in the field of image processing for its capacity to represent attributes of images in a much simpler, efficient and compact way than the traditional Fourier transform (Misiti et al ,1997), characteristics which have been increasing interest in applications o f pattern recognition in a variety areas of human activity (the finance market, identification of human voice and of fingerprints, etc.).…”

Section: Introductionmentioning

confidence: 99%

Application of the wavelet image analysis technique to monitor cell concentration in bioprocesses

Garófano

Venancio

Suazo

et al. 2005

Braz. J. Chem. Eng.

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-The growth of cells of great practical interest, such as, the filamentous cells of bacterium Streptomyces clavuligerus, the yeast Saccharomyces cerevisiae and the insect Spodoptera frugiperda (Sf9) cell, cultivated in shaking flasks with complex media at appropriate temperatures and pHs, was quantified by the new wavelet transform technique. This image analysis tool was implemented using Matlab 5.2 software to process digital images acquired of samples taken of these three types of cells throughoot their cultivation. The values of the average wavelet coefficients (AWCs) of simplified images were compared with experimental measurements of cell concentration and with computer-based densitometric measurements. AWCs were shown to be directly proportional to measurements of cell concentration and to densitometric measurements, making evident the great potential of the wavelet transform technique to quantitatively estimate the growth of several types of cells.

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Morphological characterization of filamentous microorganisms in submerged cultures by on-line digital image analysis and pattern recognition

Cited by 79 publications

References 17 publications

Mycelial growth assessment by digital image analysis in R software environment

Mycelial growth assessment by digital image analysis in R software environment

Yeast Metabolic State Identification by Fiber Optics Spectroscopy

Application of the wavelet image analysis technique to monitor cell concentration in bioprocesses

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