Methods for compensation of the light attenuation with depth of images captured by a confocal microscope

Abstract

A confocal laser scanning microscope (CLSM) enables us to capture images from a biological specimen in different depths and obtain a series of precisely registered fluorescent images. However, images captured from deep layers of the specimen may be darker than images from the topmost layers because of light loss distortions. This effect causes difficulties in subsequent analysis of biological objects. We propose a solution using two approaches: either an online method working already during image acquisition or an offline method assisting as a postprocessing step. In the online method, the gain value of a photomultiplier tube of a CLSM is controlled according to the difference of mean image intensities between the reference and currently acquired image. The offline method consists of two stages. In the first stage, a standard histogram maintaining relative frequencies of gray levels and improving brightness and contrast is created from all images in the series. In the second stage, individual image histograms are warped according to this standard histogram. The methods were tested on real confocal image data captured from human placenta and rat skeletal muscle specimens. It was shown that both approaches diminish the light attenuation in images captured from deep layers of the specimen.

Compensating Light Intensity Attenuation in Confocal Scanning Laser Microscopy by Histogram Modeling Methods

“…In Capek et al, (2006) a method for the compensation of intensity attenuation based on the warping of the histograms of the individual images in the CSLM stack to a standard histogram is introduced. The standard histogram is constructed such that warping the histograms of the individual images onto it will both preserve the high contrast of the optimally captured sections while in the same time will improve the contrast and the brightness of the low quality images in the stack.…”

“…The computation of the standard histogram is inspired by a procedure for histogram normalization proposed by Nyul et al (2000) which consists in directly matching landmarks in histograms. Unlike the original approach by Nyul et al(2000), the method proposed by Capek et al (2006) searches for the longest distance between two adjacent landmarks in one histogram. The considered landmarks correspond to the minimum intensity, maximum intensity and the n-th percentiles of the image histogram, for n= {10, 20,…, 90}.…”

“…The third method that we overview is the one of Semechko et al, (2011), who propose a method that performs intensity attenuation correction that combines aspects from the methods of Capek et. al.…”

“…The techniques that we present in this chapter can be regarded as histogram modeling methods. In order compensate light attenuation, Capek et al (2006) considered the specification for each frame in the stack of a standard histogram. The standard histogram was computed according to a normalization procedure proposed by Nyul et al (2000) which consists on matching landmarks in histograms.…”

“…The reference frame is elected by using an estimator which takes into account aspects such as brightness, contrast and sharpness. Semechko et al (2011), propose an intensity attenuation correction method which combines the ones of Capek et al (2006) and Stanciu et al (2010). In this method, the authors use as reference the standard histogram proposed in Capek (2006), specify it in its exact shape to the other images of the stack by using the algorithm of Coltuc (2006) and finally nonlinear diffusion filtering is used aiming to suppress noise and homogenize locally over-enhanced image regions.…”