Literature DB >> 18327573

Stalked protozoa identification by image analysis and multivariable statistical techniques.

A L Amaral1, Y P Ginoris, A Nicolau, M A Z Coelho, E C Ferreira.   

Abstract

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.

Entities:  

Mesh:

Year:  2008        PMID: 18327573     DOI: 10.1007/s00216-008-1845-y

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  9 in total

1.  A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers.

Authors:  Peng Zhao; Chen Li; Md Mamunur Rahaman; Hao Xu; Hechen Yang; Hongzan Sun; Tao Jiang; Marcin Grzegorzek
Journal:  Front Microbiol       Date:  2022-03-02       Impact factor: 5.640

2.  Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer.

Authors:  Jinghua Zhang; Chen Li; Yimin Yin; Jiawei Zhang; Marcin Grzegorzek
Journal:  Artif Intell Rev       Date:  2022-05-04       Impact factor: 9.588

Review 3.  Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments.

Authors:  Priya Rani; Shallu Kotwal; Jatinder Manhas; Vinod Sharma; Sparsh Sharma
Journal:  Arch Comput Methods Eng       Date:  2021-08-31       Impact factor: 8.171

4.  Identification of protozoa in dairy lagoon wastewater that consume Escherichia coli O157:H7 preferentially.

Authors:  Subbarao V Ravva; Chester Z Sarreal; Robert E Mandrell
Journal:  PLoS One       Date:  2010-12-20       Impact factor: 3.240

5.  Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species.

Authors:  Ali Soleymani; Frank Pennekamp; Owen L Petchey; Robert Weibel
Journal:  PLoS One       Date:  2015-12-17       Impact factor: 3.240

6.  Dynamic species classification of microorganisms across time, abiotic and biotic environments-A sliding window approach.

Authors:  Frank Pennekamp; Jason I Griffiths; Emanuel A Fronhofer; Aurélie Garnier; Mathew Seymour; Florian Altermatt; Owen L Petchey
Journal:  PLoS One       Date:  2017-05-04       Impact factor: 3.240

7.  BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes.

Authors:  Frank Pennekamp; Nicolas Schtickzelle; Owen L Petchey
Journal:  Ecol Evol       Date:  2015-06-04       Impact factor: 2.912

8.  A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis.

Authors:  Curtis Larimer; Eric Winder; Robert Jeters; Matthew Prowant; Ian Nettleship; Raymond Shane Addleman; George T Bonheyo
Journal:  Anal Bioanal Chem       Date:  2015-12-07       Impact factor: 4.142

9.  Automated classification of bacterial cell sub-populations with convolutional neural networks.

Authors:  Denis Tamiev; Paige E Furman; Nigel F Reuel
Journal:  PLoS One       Date:  2020-10-26       Impact factor: 3.240

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.