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Title

Model-based 2.5-d deconvolution for extended depth of field in brightfield microscopy

Authors
Aguet, Francois
Unser, Michael
Published in IEEE Transactions on Image Processing. 2008, vol. 17, no. 7, p. 1144-1153
Abstract Due to the limited depth of field of brightfield microscopes, it is usually impossible to image thick specimens entirely in focus. By optically sectioning the specimen, the in-focus information at the specimen's surface can be acquired over a range of images. Commonly based on a high-pass criterion, extended-depth-of-field methods aim at combining the in-focus information from these images into a single image of the texture on the specimen's surface. The topography provided by such methods is usually limited to a map of selected in-focus pixel positions and is inherently discretized along the axial direction, which limits its use for quantitative evaluation. In this paper, we propose a method that jointly estimates the texture and topography of a specimen from a series of brightfield optical sections; it is based on an image formation model that is described by the convolution of a thick specimen model with the microscope's point spread function. The problem is stated as a least-squares minimization where the texture and topography are updated alternately. This method also acts as a deconvolution when the in-focus PSF has a blurring effect, or when the true in-focus position falls in between two optical sections. Comparisons to state-of-the-art algorithms and experimental results demonstrate the potential of the proposed approach.
Keywords *AlgorithmsComputer SimulationImage Enhancement/*methodsImage Interpretation, Computer-Assisted/*methodsImaging, Three-Dimensional/*methodsMicroscopy/*methodsModels, TheoreticalReproducibility of ResultsSensitivity and Specificity
Identifiers
PMID: 18586622
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Research group Traitement d'images médicales (893)
Citation
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AGUET, Francois, VAN DE VILLE, Dimitri, UNSER, Michael. Model-based 2.5-d deconvolution for extended depth of field in brightfield microscopy. In: IEEE Transactions on Image Processing, 2008, vol. 17, n° 7, p. 1144-1153. https://archive-ouverte.unige.ch/unige:19621

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Deposited on : 2012-04-23

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