30 June 2017 Ziehl–Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis
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Abstract
Ziehl–Neelsen stained microscopy is a crucial bacteriological test for tuberculosis detection, but its sensitivity is poor. According to the World Health Organization (WHO) recommendation, 300 viewfields should be analyzed to augment sensitivity, but only a few viewfields are examined due to patient load. Therefore, tuberculosis diagnosis through automated capture of the focused image (autofocusing), stitching of viewfields to form mosaics (autostitching), and automatic bacilli segmentation (grading) can significantly improve the sensitivity. However, the lack of unified datasets impedes the development of robust algorithms in these three domains. Therefore, the Ziehl–Neelsen sputum smear microscopy image database (ZNSM iDB) has been developed, and is freely available. This database contains seven categories of diverse datasets acquired from three different bright-field microscopes. Datasets related to autofocusing, autostitching, and manually segmenting bacilli can be used for developing algorithms, whereas the other four datasets are provided to streamline the sensitivity and specificity. All three categories of datasets were validated using different automated algorithms. As images available in this database have distinctive presentations with high noise and artifacts, this referral resource can also be used for the validation of robust detection algorithms. The ZNSM-iDB also assists for the development of methods in automated microscopy.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Mohammad Imran Shah, Mohammad Imran Shah, Smriti Mishra, Smriti Mishra, Vinod Kumar Yadav, Vinod Kumar Yadav, Arun Chauhan, Arun Chauhan, Malay Sarkar, Malay Sarkar, Sudarshan K. Sharma, Sudarshan K. Sharma, Chittaranjan Rout, Chittaranjan Rout, } "Ziehl–Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis," Journal of Medical Imaging 4(2), 027503 (30 June 2017). https://doi.org/10.1117/1.JMI.4.2.027503 . Submission: Received: 2 March 2017; Accepted: 14 June 2017
Received: 2 March 2017; Accepted: 14 June 2017; Published: 30 June 2017
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