Model-Based 2.5-D Deconvolution for Extended Depth of Field in Brightfield Microscopy

• Published in: IEEE transactions on image processing Vol. 17; no. 7; pp. 1144 - 1153
• Main Authors: Aguet, F., Van De Ville, D., Unser, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-07-2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Biological and medical sciences; Biomedical image processing; Biomedical optical imaging; Computer Simulation; Computerized, statistical medical data processing and models in biomedicine; Convolution; Deconvolution; Depth of field; Exact sciences and technology; Focusing; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Imaging, Three-Dimensional - methods; Information, signal and communications theory; Inverse problems; Lighting; Mathematical models; Medical management aid. Diagnosis aid; Medical sciences; Microscopy - methods; Models, Theoretical; Optical microscopy; optical transfer functions; Reproducibility of Results; Sensitivity and Specificity; Signal processing; Surface layer; Surface texture; Surface topography; Telecommunications and information theory; Texture; Topography; Microscope; Image texture; State of the art; Image processing; Optical transfer function; Updating; optical transfer functions; Algorithm; Blurred image; Point spread function; Depth of field; Inverse problem; Deconvolution; Convolution; Microscopy; Least squares method; Imaging; Topography; inverse problems; Medical imagery; Biomedical image processing
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.924393

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.