Automated focusing in bright‐field microscopy for tuberculosis detection

Abstract

SummaryAutomated microscopy to detect Mycobacterium tuberculosis in sputum smear slides would enable laboratories in countries with a high tuberculosis burden to cope efficiently with large numbers of smears. Focusing is a core component of automated microscopy, and successful autofocusing depends on selection of an appropriate focus algorithm for a specific task. We examined autofocusing algorithms for bright-field microscopy of Ziehl-Neelsen stained sputum smears. Six focus measures, defined in the spatial domain, were examined with respect to accuracy, execution time, range, full width at half maximum of the peak and the presence of local maxima. Curve fitting around an estimate of the focal plane was found to produce good results and is therefore an acceptable strategy to reduce the number of images captured for focusing and the processing time. Vollath's F 4 measure performed best for full z-stacks, with a mean difference of 0.27 µm between manually and automatically determined focal positions, whereas it is jointly ranked best with the Brenner gradient for curve fitting.

An interacting multiple model filter‐based autofocus strategy for confocal time‐lapse microscopy

“…Representative results from two sets (S11 and S15) are depicted in Figure 5 for the first 150 min and reveal very high prediction accuracy. Very few scans, aside from the predefined minimum number of required scans, were necessary when compared to recent results using other methods (Brázdilová & Kozubek, 2009;Osibote et al, 2010). As mentioned, we set the minimum number of required scans for focusing to five.…”

“…Current state-of-the-art focus searching methods exploit various optimization strategies, such as adaptive step size (Brázdilová & Kozubek, 2009), function fitting (Osibote et al, 2010) and Fibonacci search (Liu et al, 2007), to attempt to reduce the number of frames required for focusing. However, none of these algorithms attempts to minimize the required number of scans by drift estimation.…”

Image Fusion Methods for Confocal Scanning Laser Microscopy Experimented on Images of Photonic Quantum Ring Laser Devices

“…The results of the experiments performed on natural images showed that the Sum-Modified Laplacian (SML) can provide better performance than other focus measures when the execution time is not included in the evaluation, but other measures such as Energy of Laplacian of the image, Tenenbaum's algorithm or Energy of image gradient provided good results as well. In (Osibote et al, 2010), a comparison of automated focusing methods for brightfield microscopy was conducted. It was showed that Vollath's F4 algorithm provided best results, but in the same time Brenner and Tenenbaum's algorithm provided very good results as well.…”

Compensating Light Intensity Attenuation in Confocal Scanning Laser Microscopy by Histogram Modeling Methods

“…Since detecting edges means detecting discontinuities, one can use derivative operators as the gradient or Laplacian. Derivative operators are commonly used as well for focus assessment in microscopy imaging (Osibote et al, 2010) and can be employed in image fusion methods (Stanciu, 2011). We have employed the Sobel edge detector (Gonzales and Woods, 2002) in the design of the automatic reference frame estimator.…”