Background: Many digital microscopy images suffer from poor illumination at the peripheries (vignetting), often attributable to factors related to the light path between the camera and the microscope. A commonly used method for correcting this has been to use the illumination of an empty field as a correction filter (white shading correction). Aims: To develop an alternative shading correction method that does not require this additional image. Methods/Results: This report outlines an alternative shading correction method that is based upon the intrinsic properties of the image, which are revealed through Gaussian smoothing. The technique can be implemented within Adobe Photoshop or other graphics editing software and works well with both haematoxylin and eosin and immunohistochemical images. Conclusions:This technique provides an effective means of optimising digital microscopy image appearances for printing, image analysis, or telepathology.

Automated microscopy can image specimens larger than the microscope’s field of view (FOV) by stitching overlapping image tiles. It also enables time-lapse studies of entire cell cultures in multiple imaging modalities. We created MIST (Microscopy Image Stitching Tool) for rapid and accurate stitching of large 2D time-lapse mosaics. MIST estimates the mechanical stage model parameters (actuator backlash, and stage repeatability ‘r’) from computed pairwise translations and then minimizes stitching errors by optimizing the translations within a (4r)2 square area. MIST has a performance-oriented implementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes of time-lapse multi-channel mosaics 15 to 100 times faster than existing tools. We created 15 reference datasets to quantify MIST’s stitching accuracy. The datasets consist of three preparations of stem cell colonies seeded at low density and imaged with varying overlap (10 to 50%). The location and size of 1150 colonies are measured to quantify stitching accuracy. MIST generated stitched images with an average centroid distance error that is less than 2% of a FOV. The sources of these errors include mechanical uncertainties, specimen photobleaching, segmentation, and stitching inaccuracies. MIST produced higher stitching accuracy than three open-source tools. MIST is available in ImageJ at isg.nist.gov.

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.y Key terms fluorescence microscopy; k-means cluster; image segmentation; cell edge; bivariate similarity index NUMEROUS areas of biomedical research rely on imaging of cells to provide information about molecular and phenotypic responses of cells to pharmaceuticals, toxins, and other environmental factors (1,2). Cell imaging is widely used in biological experiments because it can provide information on several relevant scales simultaneously. Molecular and supramolecular scales can be probed with the use of antibody and other specific affinity reagents and with fluorescent proteins. Gross phenotypic characteristics of cells that characterize their ultimate functional state can be examined in the same experiment, and often the temporal regime can be probed simultaneously. Together, these applications of cell imaging allow inference about the molecular details and the complex outcomes of the cellular biochemistry.Because of the enormous number of parameters that may influence a biological outcome, cell imaging experiments are often done in a ''high content '' mode (3,4), where large numbers of paired and replicate experiments are carried out simultaneously and result in very large (often gigabyte) image datasets. Such a large volume of image data precludes visual inspection of every image, and automated image processing and analysis is the only viable approach to data analysis.Segmentation of cell objects is a common image analysis operation that provides spatial and other features of identified objects and often precedes other operations to quantify parameters such as intracellular fluorescence. Segmentation can pose significant challenges to automated image processing and analysis. Because morphological features are often important in...