Literature DB >> 18592591

Morphological measurements on filamentous microorganisms by fully automatic image analysis.

H L Packer1, C R Thomas.   

Abstract

Characterization of mycelial morphology is important for physiological and engineering studies of filamentous fermentations, and in the design and operation of such fermentations. Image analysis has been developed as a method for this characterization, and has been shown to be faster and generally more accurate than previous methods. A fully automatic system has been developed, in which speed is gained, but with loss of accuracy in some cases. The method has been tested on Streptomyces clavuligerus and Penicillium chrysogenum P1 batch fermentations. It has also been tested on a fed-batch Penicillium chrysogenum P2 fermentation, in which the medium contained solid ingredients. Fully automatic image analysis for morphological characterization of filamentous microorganisms is an important development which will make practical many engineering and physiological studies of such fermentations that have so far not been completely satisfactory.

Entities:  

Year:  1990        PMID: 18592591     DOI: 10.1002/bit.260350904

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  6 in total

1.  Morphological quantification of filamentous fungal development using membrane immobilization and automatic image analysis.

Authors:  David J Barry; Cecilia Chan; Gwilym A Williams
Journal:  J Ind Microbiol Biotechnol       Date:  2009-03-07       Impact factor: 3.346

2.  A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.

Authors:  Pingli Ma; Chen Li; Md Mamunur Rahaman; Yudong Yao; Jiawei Zhang; Shuojia Zou; Xin Zhao; Marcin Grzegorzek
Journal:  Artif Intell Rev       Date:  2022-06-07       Impact factor: 9.588

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

Authors:  Jiawei Zhang; Chen Li; Md Mamunur Rahaman; Yudong Yao; Pingli Ma; Jinghua Zhang; Xin Zhao; Tao Jiang; Marcin Grzegorzek
Journal:  Artif Intell Rev       Date:  2021-09-29       Impact factor: 9.588

4.  Morphological development of Aspergillus niger in submerged citric acid fermentation as a function of the spore inoculum level. Application of neural network and cluster analysis for characterization of mycelial morphology.

Authors:  Maria Papagianni; Michael Mattey
Journal:  Microb Cell Fact       Date:  2006-01-25       Impact factor: 5.328

5.  The morphology of Ganoderma lucidum mycelium in a repeated-batch fermentation for exopolysaccharide production.

Authors:  Wan Abd Al Qadr Imad Wan-Mohtar; Safuan Ab Kadir; Nazamid Saari
Journal:  Biotechnol Rep (Amst)       Date:  2016-05-17

6.  SParticle, an algorithm for the analysis of filamentous microorganisms in submerged cultures.

Authors:  Joost Willemse; Ferhat Büke; Dino van Dissel; Sanne Grevink; Dennis Claessen; Gilles P van Wezel
Journal:  Antonie Van Leeuwenhoek       Date:  2017-09-15       Impact factor: 2.271

  6 in total

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