Intensity correction of fluorescent confocal laser scanning microscope images by mean‐weight filtering

Abstract

SummaryThis paper addresses the problem of intensity correction of fluorescent confocal laser scanning microscope images. Confocal laser scanning microscope images are frequently used in medicine for obtaining 3D information about specimen structures by imaging a set of 2D cross sections and performing 3D volume reconstruction afterwards. However, 2D images acquired from fluorescent confocal laser scanning microscope images demonstrate significant intensity heterogeneity, for example, due to photo-bleaching and fluorescent attenuation in depth. We developed an intensity heterogeneity correction technique that (a) adjusts the intensity heterogeneity of 2D images, (b) preserves fine structural details and (c) enhances image contrast, by performing spatially adaptive mean-weight filtering. Our solution is obtained by formulating an optimization problem, followed by filter design and automated selection of filtering parameters. The proposed filtering method is experimentally compared with several existing techniques by using four quality metrics: contrast, intensity heterogeneity (entropy) in a low frequency domain, intensity distortion in a high frequency domain and saturation. Based on our experiments and the four quality metrics, the developed meanweight filtering outperforms other intensity correction methods by at least a factor of 1.5 when applied to fluorescent confocal laser scanning microscope images.

Compensation of inhomogeneous fluorescence signal distribution in 2D images acquired by confocal microscopy

“…Unlike Mangin's (2000) method based on entropy minimization assuming a narrow intensity distribution for each tissue class, we made more realistic assumptions, tailored for confocal microscopic images. Our technique is fully automatic, which is not the case for the correction technique suggested by Lee and Bajcsy (2006), requiring the experimental assessment of the size and shape of the filtering kernel. Moreover, our approach does not need any calibration, which is necessary in some other correction methods, usually applying a lateral correction factor calculated from an image of a uniform fluorescent sample (Hovhannisyan et al, 2008;Oldmixon and Carlsson, 1993).…”

“…Moreover, the algorithm is too slow (in the order of minutes for a single image) to be of practical interest for processing stacks of CLSM images. Lee and Bajcsy (2006) proposed an intensity correction technique in CLSM images, called mean-weight filtering. Their method is based on searching for an optimal, spatially adaptive, intensity transformation that maximizes intensity contrast with respect to background, minimizes overall spatial intensity variation for large area, and minimizes distortion of intensity gradient for local features.…”

“…Spatially varying brightness mapping was dealt with in various fields of biomedical imaging applying different approaches (Hovhannisyan et al, 2008;Lee and Bajcsy, 2006;Mangin, 2000). Mangin (2000) used entropy minimization for automatic correction of intensity nonuniformity in magnetic resonance (MR) images.…”

Spatial quantitation of FISH signals in diploid versus aneuploid nuclei

“…Additionally, most microscopy techniques suffer from inherent limitations such as Z‐anisotropy wherein the axial resolution is not equivalent to the lateral resolution, chromatic shifts, and intensity attenuation with depth. Techniques such as chromatic shift correction , depth based intensity correction , and up‐sampling in the z ‐direction are typically used for correction. We have performed minimal data pre‐processing limited to median filtering (radius = 2), followed by manual thresholding to generate the 3D point clouds required for surface estimation via superquadric modeling.…”

Toward Automated Analysis of Biofilm Architecture: Bias Caused by Extraneous Confocal Laser Scanning Microscopy Images

“…2A). Applying an algorithm for intensity correction (29,47) to z-stacks prior to image analysis would be expected to minimize the presence of such images. However, even when the extraneous images 14 through 19 were excluded, the Otsu threshold and biovolume were still affected when extraneous images with a mean pixel value of zero were present (Fig.…”