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Reseach Article

Automatic Identification and Classification of Bacilli Bacterial Cell Growth Phases

Published on None 2010 by P.S. Hiremath, Parashuram Bannigidad
Recent Trends in Image Processing and Pattern Recognition
Foundation of Computer Science USA
RTIPPR - Number 1
None 2010
Authors: P.S. Hiremath, Parashuram Bannigidad
d5bd40da-4856-427e-a639-b51b08741933

P.S. Hiremath, Parashuram Bannigidad . Automatic Identification and Classification of Bacilli Bacterial Cell Growth Phases. Recent Trends in Image Processing and Pattern Recognition. RTIPPR, 1 (None 2010), 48-52.

@article{
author = { P.S. Hiremath, Parashuram Bannigidad },
title = { Automatic Identification and Classification of Bacilli Bacterial Cell Growth Phases },
journal = { Recent Trends in Image Processing and Pattern Recognition },
issue_date = { None 2010 },
volume = { RTIPPR },
number = { 1 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 48-52 },
numpages = 5,
url = { /specialissues/rtippr/number1/975-98/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Recent Trends in Image Processing and Pattern Recognition
%A P.S. Hiremath
%A Parashuram Bannigidad
%T Automatic Identification and Classification of Bacilli Bacterial Cell Growth Phases
%J Recent Trends in Image Processing and Pattern Recognition
%@ 0975-8887
%V RTIPPR
%N 1
%P 48-52
%D 2010
%I International Journal of Computer Applications
Abstract

A major challenge in microbial ecology is to develop reliable and facile methods of computer assisted microscopy that can analyze digital images of complex microbial communities at single cell resolution, and compute useful quantitative characteristics of their organization and structure without cultivation. The objective of the present study is to develop an automatic tool to identify and classify the bacterial growth phases of bacilli cells in digital microscopic cell images. Geometric features are used to identify the different growth phases of bacilli bacterial cells, namely, normal, grownup and about-to-divide. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for bacterial cell classification based on their different growth phases by segmenting digital bacilli bacterial cell images and extracting geometric features for cell growth phase identification and classification using 3σ. classifier, k-NN classifier, Neural Network classifiers and Fuzzy classifiers. The experimental results are compared with the manual results obtained by the microbiology expert and demonstrate the efficacy of the proposed method.

References
  1. Aneja, K. R. (2002). Experiments in Microbiology Plant Pathology Tissue Culture and Mushroom Culture, Newage International Publications, New Delhi, India.
  2. Carolina Wahlby, et al., (2002). “Algorithms for cytoplasm segmentation of fluorescence labeled cells”, Analytical Cellular Pathology, 24, 101-111.
  3. Dennis Kunkel Microscopy, Inc, Science Stock Photography, http://denniskunkel.com/DK/Bacteria/
  4. Hiremath P. S. and Parashuram Bannigidad, (2009). “Automated Gram-staining Characterization of Digital Bacterial Cell Images”, Proc. Int’l. Conf. on Signal and Image Processing ICSIP 2009, pp. 209-211.
  5. Hiremath P.S. and Parashuram Bannigidad (2010). “Automatic identification and classification of bacilli bacterial cell growth phases in digital microscopic images”, National Seminar on Recent Trends in Image Processing and Pattern Recognition (RTIPPR-2010) , Feb. 15th and 16th, 2010, Bidar, pp.56-59.
  6. Hiremath P.S. and Parashuram Bannigidad (2010). “Automatic identification and classification of Bacterial Cells on Digital Microscopic Images”, 2nd International Conference on Digital Image Processing (ICDIP-2010), Proc. of SPIE Vol. 7546-53, Feb. 26-28, 2010, Singapore, pp.754613-1-6.
  7. Liu, J. F.B. Dazzo, O. Glagovela, B. Yu, A.K. Jain (2001). CMEIAS: A Computer-Aided System for the Image Analysis of Bacterial Morphotypes in Microbial Communities, Springer-Verlag, Microb. Ecol. 41: pp. 173-194.
  8. Madigon M.T. et al., Biology of Microorganism, 8th Ed. McGraw Hill Inc., Newyork (1999).
  9. Nicholas Blackburn, et al., (1998). “Rapid Determination of Bacterial Abundance, Biovolume, Morphology, and Growth by Neural Network-Based Image analysis”, Applied and Environmental Microbiology, 64(9), 3246-3255.
  10. Pattan Prakash C., V.D. Mytri and P.S. Hiremath. (2010) “Classification of Cast Iron based Graphite Grain Morpology using Neural Network Approach”, 2nd International Conference on Digital Image Processing (ICDIP-2010), Proc. of SPIE Vol. 7546-53, Feb. 26-28, 2010, Singapore.
  11. Petra Perner, (2001). “Classification of HEp-2 Cells using Fluorescent Image Analysis and Data Mining”, Medical Data Analysis, Springer Verlag, LNCS 2199, pp.219-224.
  12. Rafael C. Gonzalez and Richard E. Woods (2002). Digital Image Processing, Pearson Education Asia.
  13. Sigal Trattner and Greenspan (2004). “Automatic Identification of Bacterial Types Using Statistical Imaging methods”, IEEETransactions on Medical Imaging”, 23(7), 807-820.
  14. Thomas Posch et al. (2009). “New image analysis tool to study biomass and morphotypes of three major bacterioplankton groups in an alpine lake”, Acuatic Microbiol Ecology, 54: pp. 113-126.
  15. Venkataraman, S., et al., (2006). “Automated image analysis of atomic microscopy images of rotavirus particles”, Ultramicroscopy, Elsevier, 106, 829-837.
  16. S.Osher and J.A.Sethian , “Fronts propagating with curvature dependent speed : Algorithm based on Hamilton Jacobi Formulation” J. Compact Phy, Vol. 79, 1988, pp.12-49.
Index Terms

Computer Science
Information Sciences

Keywords

Cell classification segmentation bacterial image analysis bacilli cell growth phases k-NN classifier Neural Network classifier 3σ classifier Fuzzy classifier