Image Enhancement 

After an image stack has been acquired it may be preprocessed to improve image quality prior to 3-D reconstruction. 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 stack.

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 stack consists of fine structural components of a specimen, or when edge enhancement is desired.

The contrast and brightness of the image stack 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. 


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