ABLE: An Automated Bacterial Load Estimator for the Urinoculture Screening

Paolo Andreini, Simone Bonechi, Monica Bianchini, Andrea Garzelli, Alessandro Mecocci

2016

Abstract

Urinary Tract Infections (UTIs) are very common in women, babies and the elderly. The most frequent cause is a bacterium, called Escherichia Coli, which usually lives in the digestive system and in the bowel. Infections can target the urethra, bladder or kidneys. Traditional analysis methods, based on human experts’ evaluation, are typically used to diagnose UTIs, an error prone and lengthy process, whereas an early treatment of common pathologies is fundamental to prevent the infection spreading to kidneys. This paper presents an image based Automated Bacterial Load Estimator (ABLE) system for the urinoculture screening, that provides quick and traceable results for UTIs. Infections are accurately detected and the bacterial load is evaluated through image processing techniques. First, digital color images of the Petri dishes are automatically captured, and cleaned from noisily elements due to laboratory procedures, then specific spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate evaluation of the infection severity is performed. A dataset of 499 urine samples has been used during the experiments and the obtained results are fully discussed. The ABLE system speeds up the analysis, grants repeatable results, contributes to the process standardization, and guarantees a significant cost reduction.

References

  1. Agah, A., editor (2014). Artificial Intelligence in Healthcare. CRC Press.
  2. Ahuja, K. and Tuli, P. (2013). Object recognition by template matching using correlations and phase angle method. International Journal of Advanced Research in Computer and Communication Engineering, 2(3):1368-1373.
  3. Andreini, P., Bonechi, S., Bianchini, M., Mecocci, A., and Di Massa, V. (2015). Automatic image classification for the urinoculture screening. In Smart Innovation, Systems and Technologies, volume Intelligent Decision Technologies, 39, pages 31-42.
  4. Ballabio, C., Venturi, N., Scala, M. R., Mocarelli, P., and Brambilla, P. (2010). Evaluation of an automated method for urinoculture screening. Microbiologia Medica, 5(3):178-180.
  5. Bandinelli, N., Bianchini, M., and Scarselli, F. (2012). Learning long-term dependencies using layered graph neural networks. In Proceedings of IJCNN-WCCI 2012, pages 1-8.
  6. Belazzi, R., Diomidous, M., Sarkar, I. N., Takabayashi, K., Ziegler, A., McCray, A. T., and Sim, I. (2011). Data analysis and data mining: Current issues in biomedical informatics support systems. Methods Inf. Med., 50(6):536-544.
  7. Berlin, A., Sorani, M., and Sim, I. (2006). A taxonomic description of computer-based clinical decision support systems. J. Biomedical Informatics, 39:657-667.
  8. Bianchini, M., Maggini, M., and Jain, L. C., editors (2013). Handbook on Neural Information Processing, volume Intelligent Systems Reference Library, 49. Springer.
  9. Bourbeau, P. P. and Ledeboer, N. A. (2013). Automation in clinical microbiology. Journal of Clinical Microbiology, 51(6):1658-1665.
  10. Broerm, M. A., Bahçeci, S., Vader, H. L., and Arents, N. L. (2011). Screening for urinary tract infections with the sysmex uf-1000i, urine flow cytometer. Journal of Clinical Microbiology, 49:1025-1029.
  11. Brugger, S. D., Baumberger, C., Jost, M., Jenni, W., Brugger, U., and Múhlemann, K. (2012). Automated counting of bacterial colony forming units on Agar plates. PLoS ONE, 7(3):e33695.
  12. Chen, W.-B. and Zhang, C. (2009). An automated bacterial colony counting and classification system. Inf. Syst. Front., 11(4):349-368.
  13. Clarke, M. L., Burton, R. L., Hill, A. N., Litorja, M., Nahm, M. H., and Hwang, J. (2010). Low-cost, highthroughput, automated counting of bacterial colonies. Cytometry Part A, 77(8):790-797.
  14. Deserno, T. M., editor (2011). Biomedical Image Processing. Springer-Verlag, New York.
  15. Dey, D. K., Ghosh, S., and Mallick, B. K. (2010). Bayesian Modeling in Bioinformatics. CRC Press.
  16. Gonzalez, R. and Woods, R. (2008). Digital Image Processing. Addison Wesley.
  17. Heckerling, P. S., Canaris, G. J., Flach, S. D., Tape, T. G., Wigton, R. S., and Gerber, B. S. (2007). Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. Int. J. Med. Inform., 76(4):289-296.
  18. Henze, N. and Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics - Theory and Methods, 19(10):3595-3617.
  19. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3):519-530.
  20. NHS Purchasing and Supply Agency (2011). Automated urine screening systems.
  21. Ogawa, H., Nasu, S., Takeshige, M., Funabashi, H., Saito, M., and Matsuoka, H. (2012). Noise-free accurate count of microbial colonies by time-lapse shadow image analysis. Journal of Microbiological Methods, 91(43):420-428.
  22. Otsu, N. (1979). A threshold selection method from graylevel histograms. IEEE Trans. Sys. Man Cyber., 9:62- 66.
  23. Rice, F. and Baruch, A. (2009). Evaluation of BioMérieuxs PREVI Isola, an automated microbiology specimen processor: Improving efficiency and quality of results.
  24. Torres, A. and Nieto, J. J. (2006). Fuzzy logic in medicine and bioinformatics. J. of Biomedicine and Biotechnology, (91908).
  25. Wang, W. (2011). Colony image acquisition system and segmentation algorithms. Optical Engineering, 50(12):123001-123010.
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Paper Citation


in Harvard Style

Andreini P., Bonechi S., Bianchini M., Garzelli A. and Mecocci A. (2016). ABLE: An Automated Bacterial Load Estimator for the Urinoculture Screening . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 573-580. DOI: 10.5220/0005687005730580


in Bibtex Style

@conference{icpram16,
author={Paolo Andreini and Simone Bonechi and Monica Bianchini and Andrea Garzelli and Alessandro Mecocci},
title={ABLE: An Automated Bacterial Load Estimator for the Urinoculture Screening},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={573-580},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005687005730580},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - ABLE: An Automated Bacterial Load Estimator for the Urinoculture Screening
SN - 978-989-758-173-1
AU - Andreini P.
AU - Bonechi S.
AU - Bianchini M.
AU - Garzelli A.
AU - Mecocci A.
PY - 2016
SP - 573
EP - 580
DO - 10.5220/0005687005730580