Open AccessCONTENT-BASED AUTOFOCUSING IN AUTOMATED MICROSCOPY
Author(s) -
Peter Hamm,
Janina Schulz,
Kurt Englmeier,
Helmholtz Zentrum
Publication year - 2010
Publication title -
image analysis and stereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 27
eISSN - 1854-5165
pISSN - 1580-3139
DOI - 10.5566/ias.v29.p173-180
Subject(s) - autofocus , differential interference contrast microscopy , focus (optics) , computer science , artificial intelligence , microscopy , computer vision , robustness (evolution) , microscope , phase contrast microscopy , depth of field , optics , physics , chemistry , biochemistry , gene
Autofocusing is the fundamental step when it comes to image acquisition and analysis with automated microscopy devices. Despite all efforts that have been put into developing a reliable autofocus system, recent methods still lack robustness towards different microscope modes and distracting artefacts. This paper presents a novel automated focusing approach that is generally applicable to different microscope modes (bright-field, phase contrast, Differential Interference Contrast (DIC) and fluorescence microscopy). The main innovation consists in a Content-based focus search that makes use of a priori knowledge about the observed objects by employing local object features and Boosted Learning. Hence, this method turns away from common autofocus approaches that apply solely whole image frequency measurements to obtain the focus plane. Thus, it is possible to exclude artefacts from being brought into focus calculation as well as locating the in-focus layer of specific microscopic objects