Automation and Diagnostic Support for Tuberculosis in Bacilloscopy
Abstract
This paper describes workinprogress on computersupported diagnosis of tuberculosis through Baciloscopy. The following automated procedure is used: (a) acquisition of image stacks taken at different focal depths in various XY slide positions in an automated microscope, (b) images transmission to a cluster, (c) application of deconvolution algorithms to reduce the pointspread function effect of the light microscope, (d) application of segmentation algorithms for selection of images with bacilli sugestive structures. The result obtained by the automated process is finally presented to the specialist for a definitive diagnosis.References
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Wu, Q., Merchant, F. e Castleman, K. R. (2008) “Microscope Image Processing”, United Kingdom, Elsevier Science & Technology, pp. 334-355.
Biggs, D. S. C. (1998) “Accelerated iterative blind deconvolution”, PhD thesis, New Zealand: University of Auckland, 1998, pp. 13 – 48.
Brett-Major, D. M. e Walsh, T. E. (2006) “Laboratory Diagnosis of Tuberculosis in Primary Care”, Disease-a-Month, 52-11, pp. 450-458.
Conchello, J. e Lichtman, J. W. (2005), “Optical sectioning microscopy”, Nature Methods, 2, pp. 920 – 931.
Forero, M. G., Cristóbal, G. e Alvarez-Borrego, J. (2003) “Automatic identification techniques of tuberculosis bacteria”, Applications of Digital Image Processing, XXVI Proceedings of the SPIE, Santa Clara, pp. 71-81.
Hadley, A. (2009), CombineZ - Frame Stacking - free software, [link], Maio.
Hilsenstein, V. (2005) “Robust Autofocusing for Automated Microscopy Imaging of Fluorescently Labelled Bacteria”, Digital Image Computing: Techniques and Applications Proceedings, DICTA-2005, Cairns-FR, pp. 95 – 10.
Hiraoka, Y., Sedat, J. W. e Agard, D. A. (1990) “Determination of the three-dimensional imaging properties of an optical microscope system: partial confocal behavior in epifluorescence microscopy”, Biophysical Journal, 57-2, pp. 325-333.
Hripcsak, G., Knirsch, C. A. e Pablos-Mendez, A. (1997) “Automated tuberculosis detection”, Journal of the American Medical Informatics Association, 4-5, pp. 376-381.
Ministério da Saúde (2008) “Manual Nacional de Vigilância Laboratorial da Tuberculose e outras Microbactérias”, pp. 127-178.
Pneuvmatikos, D., Markatos, E. P. , Maglis, G. e Ioannidis, S. (1998), “On Using Network RAM as a non-volatile Buffer”, Technical report 227, [link]
Russell, M. J. e Douglas, T. S. (2007) “Evaluation of autofocus algorithms for tuberculosis microscopy”, Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE, Lyon-FR, pp. 3489 – 3492.
Sarder, P. e Nehorai, A. (2006) “Deconvolution methods for 3-D fluorescence microscopy images”, Signal Processing Magazine, 23-3 , pp. 32-45.
Steingart, K. R. et all (2006) “Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review”, The Lancet Infectious Diseases, 6-9, pp. 570-581.
World Health Organization (2009) “Global Tuberculosis Control: Surveillance, Planning, Financing”, [link].
Wu, Q., Merchant, F. e Castleman, K. R. (2008) “Microscope Image Processing”, United Kingdom, Elsevier Science & Technology, pp. 334-355.
Published
2009-07-20
How to Cite
COLNAGO, Bruna V.; MAIA, João V. R.; BERGER, Mariella; BORTOLON, Saulo; MARTINELLO, Magnos; CÔCO, Klaus F..
Automation and Diagnostic Support for Tuberculosis in Bacilloscopy. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 9. , 2009, Bento Gonçalves/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
.
p. 2081-2084.
ISSN 2763-8952.
