Detection of Mycobacterium Tuberculosis in Ziehl-Neelsen Sputum Smear Images

Tuberculosis is one of the top 10 causes of death worldwide. In order to reduce the workload of doctors and the probability of human error, this paper presents an automatic detection algorithm for Mycobacterium tuberculosis in Ziehl-Neelsen Sputum Smear Images. The algorithm uses color feature and three morphological characters, which are aspect ratio, circularity and area. Background Equalization algorithm is proposed to utilize color feature sufficiently. This algorithm takes advantage of the watershed algorithm and the channel “a” in Lab color space. Experimental results confirmed the high accuracy of the proposed algorithm.

[1]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Jitendra Malik,et al.  Using contours to detect and localize junctions in natural images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Tania S. Douglas,et al.  Detection of tuberculosis in sputum smear images using two one-class classifiers , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  T S Douglas,et al.  Automated detection of tuberculosis in Ziehl‐Neelsen‐stained sputum smears using two one‐class classifiers , 2010, Journal of microscopy.

[5]  Yunhui Liu,et al.  Automatic identification of mycobacterium tuberculosis from ZN-stained sputum smear: Algorithm and system design , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.

[6]  Tania S. Douglas,et al.  Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears , 2010, IEEE Transactions on Information Technology in Biomedicine.

[7]  Muhammad Khusairi Osman,et al.  Combining threshoding and clustering techniques for Mycobacterium tuberculosis segmentation in tissue sections , 2011 .

[8]  Marly Guimarães Fernandes Costa,et al.  Mycobacterium tuberculosis recognition with conventional microscopy , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Xu Cha.  A segmentation algorithm for optical microscopic mycobacterium tuberculosis images based on region segmentation , 2014 .

[10]  L. Gabbasova,et al.  Global tuberculosis report (2014) , 2014 .

[11]  Yunhui Liu,et al.  A segmentation algorithm for Mycobacterium Tuberculosis images based on automatic-marker watershed transform , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[12]  Marly Guimarães Fernandes Costa,et al.  A sputum smear microscopy image database for automatic bacilli detection in conventional microscopy , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  P. Gupta,et al.  Automatic Detection of Mycobacterium tuberculosis inStained Sputum and Urine Smear Images , 2015 .

[14]  Rani Oomman Panicker,et al.  Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods , 2018 .