368 8.5  Advanced In Silico Analysis Tools

8.5  ADVANCED IN SILICO ANALYSIS TOOLS

Computational analysis of images is of core importance to any imaging technique, but the

most basic light microscopy technique. The challenge here is to automate and objectify

detection and quantitation. In particular, advances in low-​light fluorescence microscopy

have allowed single-​molecule imaging experiments in living cells across all three domains

of life to become commonplace. Many open-​source computational software packages are

available for image analysis, the most popular of which is ImageJ, which is based on a Java

platform and has an extensive back catalog of software modules written by members of the

user community. C/​C+​+​ is a popular computational language of choice for dedicated com­

putationally efficient image analysis, while the commercial language MATLAB is ideal for

the development of new image analysis routines as it benefits from extensive image analysis

coding libraries as well as an enthusiastic user community that regularly exchanges ideas and

new beta versions of code modules.

Single-​molecule live-​cell data are typically obtained in a very low signal-​to-​noise ratio

regime often only marginally in excess of 1, in which a combination of detector noise, sub­

optimal dye photophysics, natural cell autofluorescence, and underlying stochasticity of

biomolecules results in noisy datasets for which the underlying true molecular behavior

is nontrivial to observe. The problem of faithful signal extraction and analysis in a noise-​

dominated regime is a “needle in a haystack” challenge, and experiments benefit enormously

from a suite of objective, automated, high-​throughput analysis tools that detect underlying

molecular signatures across a large population of cells and molecules. Analytical computa­

tional tools that facilitate this detection comprise methods of robust localization and tracking

of single fluorescently labeled molecules, analysis protocols to reliably estimate molecular

complex stoichiometry and copy numbers of molecules in individual cells, and methods to

objectively render distributions of molecular parameters. Aside from image analysis compu­

tational tools, there are also a plethora of bioinformatics computational tools that can assist

with determining molecular structures in particular.

8.5.1  IMAGE PROCESSING, SEGMENTATION, AND RECOGNITION

The first stage of image processing is generally the eradication of noise. The most common

way to achieve this is by applying low-​pass filters in the Fourier space of the image, generated

from the discrete Fourier transform (DFT), to block higher spatial frequency components

characteristic of pixel noise, and then reconstructing a new image using the discrete inverse

Fourier transform. The risk here is a reduction in equivalent spatial resolution. Other

methods of image denoising operate in real space, for example, using 2D kernel convolu­

tion that convolves each pixel in the image with a well-​defined 2D kernel function, with the

effect of reducing the noise on each pixel by generating a weighted averaging signal output

involving it and several pixels surrounding it, though as with Fourier filtering methods a

caveat is often a reduction in spatial resolution manifest as increased blurring on the image,

though some real space denoising algorithms incorporate components of feature detection

in images and so can refine the denoising using an adaptive kernel, that is, to increase the

kernel size in relatively featureless regions of an image but decrease it for regions suggesting

greater underlying structure.

Several other standard image processing algorithms may also be used in the initial stages

of image analysis. These include converting pixel thresholding to convert raw pixel inten­

sity into either 0 or 1 values, such that the resulting image becomes a binary large object

(BLOB). A range of different image processing techniques can then be applied to BLOBs. For

example, erosion can trim off outer pixels in BLOB hotspots that might be associated with

noise, whereas dilation can be used to expand the size of BLOBs to join proximal neighboring

BLOBs into a single BLOB, the reason being again that this circumvents the artifactual effects

of noise that can often have an effect to make a single foreground object feature in an image

that appear to be composed of multiple separate smaller foreground objects.