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Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis

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Abstract

Tuberculosis is a terrible, transferrable disease instigated by infection with Mycobacterium tuberculosis bacillus. A common diagnosis of this infection is the microscopic examination of sputum smears of affected persons. This paper presents an automatic system for detecting the bacillus present in the stained microscopic images of sputum which eases the job of laboratory technician with manual detection. In this work, the Channel Area Thresholding algorithm is proposed for segmentation of the bacterial image. The intensity-based local bacilli features are extracted using location-oriented histogram and speeded up robust feature algorithm. The exact classification of the bacilli objects after segmentation is carried out using deep learning neural networks. Experimental results show that the proposed work had the classification accuracy of about 97.55 on sputum image dataset.

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Authors and Affiliations

  1. Department of Computer Science, NMCC, Marthandam, Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    K. S. Mithra & W. R. Sam Emmanuel

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  1. K. S. Mithra
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  2. W. R. Sam Emmanuel
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Correspondence to K. S. Mithra.

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Mithra, K.S., Sam Emmanuel, W.R. Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis. SIViP 13, 1585–1592 (2019). https://doi.org/10.1007/s11760-019-01509-1

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  • DOI: https://doi.org/10.1007/s11760-019-01509-1

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