title: Intensity correction of fluorescent confocal laser scanning microscope images by mean-weight filtering.
abstract: This 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 mean-weight filtering outperforms other intensity correction methods by at least a factor of 1.5 when applied to fluorescent confocal laser scanning microscope images.
authors.affiliation: National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.
authors.forename: Sang-Chul
authors.initials: SC
authors.lastname: Lee
authors.forename: Peter
authors.initials: P
authors.lastname: Bajcsy
mesh_terms: D001699:Biometry; D007090:Image Interpretation, Computer-Assisted; D018613:Microscopy, Confocal / Q000379:methods* / Q000706:statistics & numerical data; D008856:Microscopy, Fluorescence / Q000379:methods / Q000706:statistics & numerical data