Image analysis pitfals and
good practices
Pixels - Bit depth
Pixels - saturation, compression, offset
• http://fiji.sc/Detect_Information_Loss
• Overexposure/Underexposure
• depend on light source, aperture, shutter time
• histogram should be close to centered normal distribution
Colors
1.
Printing color images on BW
printer
1.
2.
Not every colorscale is
printed properly in
grayscale
Use one-color scales
source: http://uk.mathworks.com/help/matlab/ref/colormap.html
Colors
http://fiji.sc/Colocalization_Analysis
Image formats
• Filesize depends on data compression algorithm
• Lossless
• PNG
• TIF (!) - None, PackBits, LZW, ZIP, CCITT
• Lossy
• jpeg
• TIFF - jpeg
• Use RGB colorspace to avoid color conversions
(may be lossy)
Source: https://en.wikipedia.org/wiki/Color_vision
Size of the pixel - spatial calibration
• Get the mean area of droplets
• Add scale to picture
We know:
• Picture was taken on zoom 1.5
• Microscope was calibrated for zoom 1.0
• Ruler accuracy was 0.1 mm
Best practices of digital data handling
• Save raw data, work on copies
• Simple brightness/contrast adjustments are acceptable when applied
uniformly for entire image (but one should control histogram to avoid
saturation)
• Cropping and resizing images is acceptable but do not forget to
provide information on pixel size
• Digital filtering should not be used as it can mask important
information. If one needs to use it, it should be clearly justified in
documentation.
Best practices of digital data handling
• Combining images is acceptable only if it is clear to reader that the
images are separate (left space among them).
• Do not modify content of image (cloning, copying, etc.), do not
process the image selectively (ROI filtering)
• When comparing digital images, it is important that each has been
acquired under identical conditions. If the background or color
balance must be adjusted among images within a group, this must be
acknowledged in the figure.
• Quantitative analysis of images should always be performed on
uniformly processed image data, and the data should be calibrated to
a known standard.
Coloc 2 plugin
• Refer to http://fiji.sc/Coloc2 to Coloc 2 documentation
• Use provided data to perform colocalization analysis
Literature
• K. W. Dunn, M. M. Kamocka, and J. H. Mcdonald, “A practical guide to
evaluating colocalization in biological microscopy” vol. 46202, pp. 723–742,
2011.
• S. V Costes, D. Daelemans, E. H. Cho, Z. Dobbin, G. Pavlakis, and S. Lockett,
“Automatic and quantitative measurement of protein-protein
colocalization in live cells” Biophys. J., vol. 86, no. 6, pp. 3993–4003, 2004.
• E. M. M. Manders, F. J. Verbeek, and J. A. Ate, “Measurement of colocalisation of objects in dual-colour confocal images” Journal of
microscopy, vol. 169, no. 3. pp. 375–382, 1993.
• J. Adler and I. Parmryd, “Quantifying colocalization by correlation: The
pearson correlation coefficient is superior to the Mander’s overlap
coefficient” Cytom. Part A, vol. 77, no. 8, pp. 733–742, 2010.
• http://fiji.sc/Colocalization_Analysis