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

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Abstract

Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

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Acknowledgments

The authors would like to thank Dr. Marly Costa and Center for Research and Development in Electronic and Information Technology (CETELI), Federal University of Amazonas – UFAM, Brazil, for providing the TB image database for our research.

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Mangalore, India

    Rani Oomman Panicker, Gagan Saini & Jeny Rajan

  2. College of Engineering, Trikaripur, Kerala, India

    Rani Oomman Panicker

  3. AMCHSS, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, India

    Biju Soman

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  1. Rani Oomman Panicker
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Correspondence to Rani Oomman Panicker.

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Panicker, R.O., Soman, B., Saini, G. et al. A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images. J Med Syst 40, 17 (2016). https://doi.org/10.1007/s10916-015-0388-y

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