title: A statistical approach for intensity loss compensation of confocal microscopy images.
abstract: In this paper, a probabilistic technique for compensation of intensity loss in confocal microscopy images is presented. For single-colour-labelled specimen, confocal microscopy images are modelled as a mixture of two Gaussian probability distribution functions, one representing the background and another corresponding to the foreground. Images are segmented into foreground and background by applying Expectation Maximization algorithm to the mixture. Final intensity compensation is carried out by scaling and shifting the original intensities with the help of parameters estimated for the foreground. Since foreground is separated to calculate the compensation parameters, the method is effective even when image structure changes from frame to frame. As intensity decay function is not used, complexity associated with estimation of the intensity decay function parameters is eliminated. In addition, images can be compensated out of order, as only information from the reference image is required for the compensation of any image. These properties make our method an ideal tool for intensity compensation of confocal microscopy images that suffer intensity loss due to absorption/scattering of light as well as photobleaching and the image can change structure from optical/temporal section-to-section due to changes in the depth of specimen or due to a live specimen. The proposed method was tested with a number of confocal microscopy image stacks and results are presented to demonstrate the effectiveness of the method.
authors.affiliation: Computer Science and Engineering Department, University of Texas at Arlington, USA.
authors.forename: S
authors.initials: S
authors.lastname: Gopinath
authors.forename: Q
authors.initials: Q
authors.lastname: Wen
authors.forename: N
authors.initials: N
authors.lastname: Thakoor
authors.forename: K
authors.initials: K
authors.lastname: Luby-Phelps
authors.forename: J X
authors.initials: JX
authors.lastname: Gao