Exact focusing is essential for any automatic image capturing system. Performances of focus measure functions (FMFs) used for autofocusing are sensitive to image contents and imaging systems. Therefore, identification of universal FMF assumes a lot of significance. Eight FMFs were hybridized in pairs of two and implemented simultaneously on a single stack to calculate the hybrid focus measure. In total, 28 hybrid FMFs (HFMFs) and eight FMFs were implemented on stacks of images from three different imaging modalities. Performance of FMFs was found to the best at 50% region sampling. Accuracy, focus error, and false maxima were calculated to evaluate the performance of each FMF. Nineteen HFMFs provided >90% accuracy. Image distortion (noise, contrast, saturation, illumination, etc.) was performed to evaluate robustness of HFMFs. Hybrid of tenengrad variance and steerable filter-based (VGRnSFB) FMFs was identified as the most robust and accurate function with an accuracy of ≥90% and a relatively lower focus error and false maxima rate. Sharpness of focus curve of VGRnSFB along with eight individual FMFs was also computed to determine the efficacy of HFMF for the optimization process. VGRnSFB HFMF may be implemented for automated capturing of an image for any imaging system.
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.
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