The Automated Identification of Tubercle Bacilli using Image Processing and Neural Computing Techniques

Tuberculosis is currently the world’s leading cause of adult death from a single infectious disease. Sputum examination remains the cornerstone of diagnosis in epidemic situations. To improve the diagnostic process we are developing an automated method for the detection of tubercle bacilli in clinical specimens, principally sputum smears. A preliminary investigation is presented here, which makes use of image processing techniques and neural network classifiers for the automatic identification of TB bacilli on Auramine stained sputum specimens. Currently, the developed system shows a sensitivity of 93.5% for the identification of individual bacilli. As there are usually fairly numerous TB bacilli in the sputum of patients with active pulmonary TB, the overall diagnostic accuracy for sputum smear positive patients is expected to be very high. Potential benefits of automated screening for TB are rapid and accurate, diagnosis, increased screening of the population, and reduced health risk to staff processing slides.

[1]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  C. Collins,et al.  Organization and Practice in Tuberculosis Bacteriology , 1987 .

[3]  J. Selkon.  Organisation and Practice in Tuberculosis Bacteriology , 1986 .

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Anthony P. Reeves,et al.  Three-Dimensional Shape Analysis Using Moments and Fourier Descriptors , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[7]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[8]  Dwayne Phillips.  Image processing in C: analyzing and enhancing digital images , 1994 .

[9]  P. Pfitzer,et al.  Morphological study of bacteria of the respiratory system using fluorescence microscopy of Papanicolaou‐stained smears with special regard to the identification of Mycobacteria sp. , 1995, Cytopathology : official journal of the British Society for Clinical Cytology.

[10]  Huan Liu,et al.  Book review: Machine Learning, Neural and Statistical Classification Edited by D. Michie, D.J. Spiegelhalter and C.C. Taylor (Ellis Horwood Limited, 1994) , 1996, SGAR.

[11]  Tuberculosis control in South Africa--time for a new paradigm? , 1996, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.

[12]  F. Vicino.  The Probabilistic Neural Network. , 1998, Substance use & misuse.