Literature DB >> 10560504

Automated identification of tubercle bacilli in sputum. A preliminary investigation.

K Veropoulos1, G Learmonth, C Campbell, B Knight, J Simpson.   

Abstract

OBJECTIVE: To use an automated method to detect tubercle bacilli in sputum specimens. STUDY
DESIGN: Using fluorescence microscopy, tubercle bacilli were identified on auramine-stained sputum specimens. Images were then captured with a digital camera and enhanced through imaging processing techniques. The bacilli were recognized using neural network classifiers.
RESULTS: This preliminary investigation demonstrated a sensitivity of 94.1% for the identification of individual bacilli. As there are usually fairly numerous tubercle bacilli in the sputum of patients with active pulmonary tuberculosis, the overall diagnostic accuracy of sputum smear-positive patients can be expected to be very high.
CONCLUSION: Potential benefits of automated screening for TB bacilli are: rapid, accurate, inexpensive diagnosis; the ability to screen larger numbers of people; increased resources to monitor patients; and reduction in health risks to staff.

Entities:  

Mesh:

Year:  1999        PMID: 10560504

Source DB:  PubMed          Journal:  Anal Quant Cytol Histol        ISSN: 0884-6812            Impact factor:   0.302


  13 in total

Review 1.  A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

Authors:  Rani Oomman Panicker; Biju Soman; Gagan Saini; Jeny Rajan
Journal:  J Med Syst       Date:  2015-10-30       Impact factor: 4.460

2.  Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains.

Authors:  P Sadaphal; J Rao; G W Comstock; M F Beg
Journal:  Int J Tuberc Lung Dis       Date:  2008-05       Impact factor: 2.373

3.  Autofocus method for automated microscopy using embedded GPUs.

Authors:  J M Castillo-Secilla; M Saval-Calvo; L Medina-Valdès; S Cuenca-Asensi; A Martínez-Álvarez; C Sánchez; G Cristóbal
Journal:  Biomed Opt Express       Date:  2017-02-22       Impact factor: 3.732

4.  Automated focusing in bright-field microscopy for tuberculosis detection.

Authors:  O A Osibote; R Dendere; S Krishnan; T S Douglas
Journal:  J Microsc       Date:  2010-11       Impact factor: 1.758

5.  Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy.

Authors:  Bhavin Patel; Tania S Douglas
Journal:  Comput Methods Programs Biomed       Date:  2012-01-17       Impact factor: 5.428

6.  Automated detection of tuberculosis in Ziehl-Neelsen-stained sputum smears using two one-class classifiers.

Authors:  R Khutlang; S Krishnan; A Whitelaw; T S Douglas
Journal:  J Microsc       Date:  2010-01       Impact factor: 1.758

7.  Mobile digital fluorescence microscopy for diagnosis of tuberculosis.

Authors:  Asa Tapley; Neil Switz; Clay Reber; J Lucian Davis; Cecily Miller; John Baptist Matovu; William Worodria; Laurence Huang; Daniel A Fletcher; Adithya Cattamanchi
Journal:  J Clin Microbiol       Date:  2013-04-03       Impact factor: 5.948

Review 8.  Digital photography: a primer for pathologists.

Authors:  Roger S Riley; Jonathan M Ben-Ezra; Davis Massey; Rodney L Slyter; Gina Romagnoli
Journal:  J Clin Lab Anal       Date:  2004       Impact factor: 2.352

9.  Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears.

Authors:  Rethabile Khutlang; Sriram Krishnan; Ronald Dendere; Andrew Whitelaw; Konstantinos Veropoulos; Genevieve Learmonth; Tania S Douglas
Journal:  IEEE Trans Inf Technol Biomed       Date:  2009-09-01

10.  "Proof-of-concept" evaluation of an automated sputum smear microscopy system for tuberculosis diagnosis.

Authors:  James J Lewis; Violet N Chihota; Minty van der Meulen; P Bernard Fourie; Katherine L Fielding; Alison D Grant; Susan E Dorman; Gavin J Churchyard
Journal:  PLoS One       Date:  2012-11-29       Impact factor: 3.240

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