16-05-2012, 01:52 PM
Image analysis for biology
Image Analysis with Matlabnew.pdf (Size: 1.23 MB / Downloads: 34)
Why Image Analysis?
Biological images contain a wealth of objects and patterns, which may convey information about
underlying mechanism in biology. Take a look at the following microscopy images:
The left microscopy image shows a field of view of tissue-culture cells. One can ask: how many
cells are there in this field of view? What is the average size? How much DNA is in each of the
cells? How are the microtubule and actin cytoskeletons organized spatially? For the movie of the
speckled spindle on the right, one can ask: What is the distribution of polymer mass in the spindle?
What is the flux rate? Does it depend on the position along the spindle? Where is monomer getting
incorporated and lost?
Image processing and analysis provides a means to extract and quantify objects and patterns in
image data and obtain answers to meaningful biological questions. It offers two advantages over
traditional more manual methods of analysis: 1) Human vision, while highly sensitive, can be
easily biased by pre-conceived notions of objects and concepts; automated image analysis provides
an unbiased approach to extracting information from image data and testing hypotheses. 2) Once
an image-analysis routine is devised, it can be applied to a large number of microscopy images,
facilitating the collection of large amounts of data for statistical analysis.
Image Analysis Strategies
Image analysis involves the conversion of features and objects in image data into quantitative
information about these measured features and attributes. Microscopy images in biology are often
complex, noisy, artifact-laden and consequently require multiple image processing steps for the
extraction of meaningful quantitative information. An outline of a general strategy for image
analysis is presented below:
1) The starting point in image analysis typically involves a digital image acquired using a CCD
camera. Raw microscopy images obtained on digital CCD cameras are subject to various
imperfections of the image acquisition setup, such as noise at low light levels, uneven illumination,
defective pixels, etc… We often need to first process the image to correct for such defects and also
to enhance the contrast to accentuate features of interest in the image for subsequent analysis. In
section II, we introduce various image transformation and spatial filtering techniques that can be
used for this purpose.
2) Having corrected artifacts and enhanced contrast in the images, we can apply various
computational techniques to extract features and patterns from the images. In the following section,
we describe various tools of morphological image processing and image segmentation that can be
used for this purpose.
3) After biological important features have been segmented from images, we can then derive
quantitative information from these features and objects. MATLAB provides a set of tools that can
be used to measure the properties of regions; the matrix representation of images in MATLAB also
allows for easy manipulation of data and calculation of quantities from microscopy images.