VOLUM OMAGIAL - Facultatea de Ştiinţe ale Naturii şi Ştiinţe Agricole

Utilization of epifluorescence microscopy…/ Ovidius University Annals, Biology-Ecology Series 14: 127-137 (2010)
algorithms of CellC software for digital images,
because this have three important parts: a MATLAB
figure file of the segmented image (this can be
exported in any common image file format; a comma
separated value (CSV) - file with quantitative data of
the cells (was opened in a spreadsheet program Excel
for further analysis); a summary CSV-file with the
cell count for each of the analyzed images for a quick
overview of the analysis process (this file were only
saved in the batch processing mode). Fluorescence
microscopy digital images were analyzed and the
objects has different intensity than the background.
Commonly, this property holds true for images of
bacteria (http://sites.google.com/site/cellcsoftware/).
Fig 9. CellC’s interface
(http://sites.google.com/site/cellcsoftware/) used for
automated digital analysis of bacteria/cyanobacteria.
Furthermore, CellC software were used for two
important purposes: to calculate total object count
(e.g. DAPI stained cells) and co-localization analysis,
comparing total and specific count images of the
same location. When two images were analyzed, the
co-localization was measured by comparing which
cells are present only in the first image, and which are
visible in both of the images. The binarized result
images was saved as JPG-images, and the
enumeration results and statistics are saved as an
Excel-ready CSV file. The images was processed
one at a time, or automatically in a batch.
Graphical illustration of the analysis process
and a part of a CSV-file opened in a spreadsheet
program are given in Figure 9. The CSV-file gives,
for each cell in the image, size and intensity
information as well as information on cell
morphologies. All results produced with digital image
processing algorithms are perfectly reproducible.
The image processing methods used guarantee
that all images are analyzed using the same criteria,
and therefore results between different images are
comparable. CellC software is easy to use due to the
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included graphical user interface, and the batch
processing mode enables fast and convenient
processing of hundreds of cell images.
CellC enumerate bright cells on a dark
background (epifluorescence). We also used two
different methods to process the images: one
image/image pair at a time; several images pairs
sequentially in batch processing mode.
If the background of the image is uneven (because of
e.g. misaligned lighting), it is preferable to choose
this option.
The default option in CellC is to present the
measured parameters in pixels. By checking this box
we define how many micrometers one pixel
corresponds to, and receive all measurement results
in micrometers. The correct value of this setting
obviously depends on the imaging setup, such as on
the camera and the objective, and must be determined
outside CellC, using ImageJ to calibrate the scale.
The main technical requirement for using CellC
is the clear visual distinction between the cells to be
counted and their background, which could be
achieved relatively easy by epifluorescence
microscopy (Ardelean et al., 2009).
If darker regions exist inside cells, thresholding
may result in false holes inside cells (darker pixels
are considered background). By selecting this option,
these holes are automatically filled. Sometimes the
fill can cause worse cell cluster separation results.
Automatic removal of over/undersized cells
were selected, because CellC automatically decides
which particles are too small to be considered as real
cells. All detected objects that are smaller than 1/10
of the mean size of all objects, were removed.
Because the sizes of under/oversized particles were
known using “Analyze Measure” option of ImageJ, it
was possible to set the thresholds manually by using
the text boxes. The unit of sizes depends on the user
defined unit (pixels/μm 2 ).
The CSV data sheet consists of following
columns: cell's serial number (a unique number given
to each cell); area of cell (estimate of the cell area);
approximate volume (approximation of the volume of
the cell); length (estimate of the cell length); width
(estimate of the cell width); intensity mean, (mean
intensity of the cell); intensity maximum, (maximum
intensity of the cell); solidity (estimate of the shape of
the cell); compactness (estimate of the shape of the