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Identification of Tuberculosis bacilli using Image Processing

Published on May 2014 by Amarja Adgaonkar, Aditi Atreya, Akshay D. Mulgund, Juhi R. Nath
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International Conference on Electronics & Computing Technologies
Foundation of Computer Science USA
ICONECT - Number 1
May 2014
Authors: Amarja Adgaonkar, Aditi Atreya, Akshay D. Mulgund, Juhi R. Nath
44cba8cb-593e-466d-844c-280c48dc6d87

Amarja Adgaonkar, Aditi Atreya, Akshay D. Mulgund, Juhi R. Nath . Identification of Tuberculosis bacilli using Image Processing. International Conference on Electronics & Computing Technologies. ICONECT, 1 (May 2014), 25-28.

@article{
author = { Amarja Adgaonkar, Aditi Atreya, Akshay D. Mulgund, Juhi R. Nath },
title = { Identification of Tuberculosis bacilli using Image Processing },
journal = { International Conference on Electronics & Computing Technologies },
issue_date = { May 2014 },
volume = { ICONECT },
number = { 1 },
month = { May },
year = { 2014 },
issn = 0975-8887,
pages = { 25-28 },
numpages = 4,
url = { /proceedings/iconect/number1/16479-1429/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Electronics & Computing Technologies
%A Amarja Adgaonkar
%A Aditi Atreya
%A Akshay D. Mulgund
%A Juhi R. Nath
%T Identification of Tuberculosis bacilli using Image Processing
%J International Conference on Electronics & Computing Technologies
%@ 0975-8887
%V ICONECT
%N 1
%P 25-28
%D 2014
%I International Journal of Computer Applications
Abstract

Tuberculosis is currently the world's leading cause of death from a single infectious disease. In the case of an epidemic the only option of diagnosis remains is the sputum examination. To improve the diagnostic process we are developing an automated method for the detection of tuberculosis bacilli in clinical specimens, preferably sputum smears. Our proposed method makes use of image processing techniques and neural network classifiers for the automatic identification of TB bacilli using Auramine stained specimens of sputum. The developed system , currently shows 93. 5% sensitivity for identifying individual bacilli. There are numerous TB bacilli with active pulmonary TB in the patient's sputum. The overall diagnostic accuracy of the patients with positive smear is expected to be very high. Some potential benefits of automated screening for TB are accurate and rapid diagnosis, increased population screening and reduced health risk.

References
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  2. Identification of Tuberculosis Bacilli using image ProcessingHarshalTalathi, Jinal Patel, KakoliLaha , Mrs. J. M. Kundargi, Department Of Electronics and Telecommunication ,K. J. Somaiya College of Engineering, Mumbai, India.
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  8. An Introduction to Neural NetworksVincent Cheung&Kevin CannonsSignal & Data Compression Laboratory Electrical & Computer EngineeringUniversity of Manitoba Winnipeg, Manitoba, Canada.
Index Terms

Computer Science
Information Sciences

Keywords

Fluorescence Microscopy Pattern Matching Tuberculosis.