Chapter 5 Visualizing cells and the csk visible Light microscopy

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5.3 Video tracking

Myocytes isolated directly from hearts are log shaped, as shown below. Their speed and magnitude of contraction can be monitored with a video camera and imaging software that tracks motion of the edges. Records from such an imaging set-up are shown below.

Figure 4.14 Myocyte contraction

3. Scanning Microscopy

Laser Confocal Microscopy (Adapted from Kees van der Wulp)

Imaging of thin optical slices, and 3-D reconstructions of cells and components can be done with scanning microscopy. One type is Laser scanning confocal microscopy (LSCM). Here the laser light beam scans the specimen that has been decorated with a fluorescent label. A confocal aperture (pinhole) is placed in front of the photodetector, such that the fluorescent light (not the reflected light!) from points on the specimen that are not within the focal plane (the so called out-of-focus light) where the laser beam was focused will be largely obstructed by the pinhole. In this way, out-of-focus information (both above and below the focal plane) is greatly reduced. This becomes especially important when dealing with thick specimens. The spot that is focused on the center of the pinhole is often referred to as the "confocal spot."

A 2-D image of a small partial volume of the specimen centered around the focal plane (referred to as an optical section) is generated by performing a raster sweep of the specimen at that focal plane. As the laser scans across the specimen, the analog light signal, detected by the photomultiplier, is converted into a digital signal, contributing to a pixel-based image displayed on a computer monitor attached to the LSCM. The relative intensity of the fluorescent light, emitted from the laser-hit point, corresponds to the intensity of the resulting pixel in the image (typically 8-bit greyscale). The plane of focus (Z-plane) is selected by a computer-controlled fine-stepping motor which moves the microscope stage up and down. Typical focus motors can adjust the focal plane in as little as 0.1 micron increments. A 3-D reconstruction of a specimen can be generated by stacking 2-D optical sections collected in series.

The general setup of an entire LSCM system is shown below. It should be noted that most laser scanning confocal microscopes consist of a confocal unit attached to a conventional fluorescence microscope.

Electron Beam Scanning

An older scanning technique uses an electron beam instead of a laser, and hence produces much higher resolution images, while sacrificing some simplicity. A scanning electron micrograph of ECM below shows strands of collagen (yellow) and elastin (blue).

Image Enhancement

After an image has been acquired it may be preprocessed to improve image quality. The preprocessing usually involves application of image filters (mathematical algorithms implimented in software) to the entire data set to remove noise and artifacts, smooth or sharpen the images, or to correct for problems with contrast and/or brightness. While these filters are generally performed as preprocessing steps, they can also be carried out after a 3-D model has been reconstructed from the image. Median and Gaussian filters have the general affect of smoothing images. These are used to eliminate noise and background artifacts and to smooth sharp edges, but also tend to remove some of the detail in small objects.

Sharpening filters can be used to emphazise details in the image stack, but also have the effect of highlighting noise and other small artifacts. The application of sharpening filters is most useful when the image consists of fine structural components of a specimen, or when edge enhancement is desired.

The contrast and brightness of the image can be adjusted to enhance perception of the sampled specimen. This is usually done by changing the ramping of the grey scale values for the dataset. Histogram equilization can be used to improve contrast by a non-linear mapping of the grey levels in an image. This technique is most commonly used when the grey levels are concentrated in a small portion of the range of possible values.

It is important to realise that the application of filters to the data set can ultimately affect quantitative measurements of 3-D reconstructions produced from it. As such, the application of filters in some instances are only used for display purposes, and quantitative measurements are made on the unprocessed data.

Segmentation refers to the process of extracting the desired object (or objects) of interest from the background in an image or data volume. There are a variety of techniques that are used to do this, ranging from the simple (such as thresholding and masking) to the complex (such as edge/boundary detection, region growing and clustering algorithms.) Segmentation can be aided through manual intervention or handled automatically through software algorithms. Examples of simple forms of segmentation that can be used with confocal data include thresholding and masking.

Thresholding involves limiting the intensity values within an individual image or the entire image stack to a certain bounded range (or ranges). For example, since each pixel in an 8-bit greyscale confocal image (with values 0 [black] to 255 [white]) corresponds to fluorescence intensity at a point within the specimen, the pixels with lower values represent areas with lower fluorescence while the pixels with higher values represent brighter regions. It may be decided that all pixels below a certain value do not contribute significantly to the object(s) of interest and hence can be eliminated. This can be done by scanning the image(s) one pixel at a time, and keeping that pixel if it is above the selected intensity value, or setting it to 0 (black) if it is below that value. In a similar manner, thresholding can also be used to eliminate non-consecutive ranges of intensities while preserving the regions containing the intensities of interest.

Masking is a procedure whereby an enclosed region(s) of an image (or of the image stack) are defined for processing. This can be done either by manually tracing around the regions of interest (e.g. with a mouse in a graphics application) or by an automated routine. An easy (and useful) application of this is to use a 2-D stacked projection of an image to define the image mask. The stacked projection of the image stack is a single image that represents the sum of all of the images in the image stack (these images can usually be provided automatically from software supplied with the LSCM.) If the object of interest has a closed, continuous surface (such as that of a neuron) the stacked projection defines the absolute boundaries of the object in 2-D. A mask can be formed by either manually tracing around the boundaries of the object(s) of interest in the stacked projection or by absolute thresholding (making all intensities above a certain value white and all below this value black.) The mask can now be applied to the entire image stack, such that regions falling within the mask selection area are preserved, whereas areas outside this region are eliminated (e.g. set to 0 [black].) After the mask has been applied, thresholding and image filtering methods can be used to aid in removing the remaining undesired regions.
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