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Automated focusing in bright‐field microscopy for tuberculosis detection
Author(s) -
OSIBOTE O.A.,
DENDERE R.,
KRISHNAN S.,
DOUGLAS T.S.
Publication year - 2010
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.2010.03389.x
Subject(s) - focus (optics) , microscopy , cardinal point , mycobacterium tuberculosis , sputum , computer science , optics , artificial intelligence , tuberculosis , computer vision , physics , pathology , medicine
Summary Automated 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.