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Image processing for selected biological experiments
J. Schier, B. Kovář
ÚTIA AV ČR, v.v.i.
2
Contents
Counting of yeast colonies(Future projects):
Microarray scans
Images from the FISH analysis (Fluorescence In-Situ Hybridization)
3
Counting of yeast colonies
Where, why?
Application area: microbiology
Testing influence of substance in the growth medium on innoculated colonies (size, growth rate)
Test setup
Colonies innoculated on Petri dishes
Grown in a growth box(constant temperature and humidity)
Dishes sampled by digital camera
Yeast colonies: examples
4.2.2010 5
Growth box and imaging workplace
Area and number of colonies
Quantitative analysis of images
Manual counting
Time consumingLimited number of samplesLimited precision
Automated counting
Extreme example – densely covered dish
Problems
Darkroom – controlled environment, but…
Random factors:
Varying position of the dish
Varying illumination, zoom setting
Dispersion of colony size & morphology
Colonies are often touching each other
Two phases of dish processing
PreprocessingImage checking and thresholding
Dish localization, ROI extraction
Evaluation of characteristicsRelative area
Colony diameter estimation
Segmentation – counting of colonies
Output filtration
Preprocessing
No fancy math, but necessary:• Detect and reject faluty images!!• Localization• ROI extraction
Background thresholding
Elimination of faulty images:
Localization – dish rim?
First solution: correlation of mask with dish rim
Not sufficiently robust – rim variations (shape, width, reflections,..)
Localization - projections
Binary image
Projections
Position check – dish out of image:
Only rim – OK, use Least Squares to refine
Inner part of dish – REJECT!
Localization – cont’d
Eliminate rim, find ROI
Counting methods
Convolution methodbased on convolution with circular pattern
Fast Radial Symmetry(Loy&Zelinsky)
Orientation & magnitude image computed from gradient
Both methods need estimate of colony radiusAdaboost, Hough Transform etc. not used – noisy, learning,...
Radius estimation
Round Irregular
MinRadius, MaxRadiusradii=[.....]
Colony counting
Convolution – original flow
Colony counting I
Convolution methodradii vector→circular convolution patterns
ColonyCenters
Colony counting II
Fast radial transform(Loy&Zelinsky 2003)
Image gradient
Orientation and Magnitude Matrices
Result – symmetry matrix
Output filtration
Reject centers in the background
- “out of colony”
Use “non-maxima suppression”Dilate output image of counting, look for
common points with original output
Tool
Results - overview117 images evaluated,
containing from 9 to 106 colonies
Fast Radial Transform: 81 images – no error
105 images – all colonies detected
(some detected multiple times)
Convolution:36 images – no error
45 images - all colonies detected
(some detected multiple times)
Test data
24 images 35 images 35 images 23 images
Test data 2
23.2.2010Prezentace ZS
Results
23.2.2010Prezentace ZS
# Colonies Samples
Fast Radial Transform Convolution
Missed [%]
False[%]
Missed [%]
False[%]
0-20 24 0,2604 6,5104 3,1250 3,9063
20-25 34 0,2663 5,1931 3,4621 2,3968
25-30 37 0 4,3173 3,4137 1,6064
>30 22 1,8952 4,3478 11,4827 0,3344
Results cont’d
23.2.2010Prezentace ZS
#Colonies Samples
0-20 31
20-25 37
25-30 30
>30 19
Minimum number of colonies: 9Maximum number of colonies: 106
Results – cont’d
23.2.2010Prezentace ZS
Rel. coverage [%]
Samples
0-2 24
2-3 25
3-5 38
>5 30
Minimum coverage: 0,44%Maximum coverage: 12,48%
Detection examples
Convolution
Fast Radial Symmetry
Typical detection errors
23.2.2010Prezentace ZS
Fast Radial Symmetry
Convolution
Difficult example from the beginning…
Result:
Coverage 42.73%
Total 598 colonies
Detected 462
Missed 136(Fast Radial Symmetry)
Conclusions
Semi-automated processing of batches of Petri dish images
Two methods proposed
Interactive graphical editor of the result
Evaluation of efficiency
Improved process over manual evaluation
Outcomes of the researchTool deployed and in practical use:
Yeast Colony Group
Department of Genetics and Microbiology
Faculty of Sciences
Charles University
Journal paper in review process:Computer Methods and Programs in
Biomedicine (Elsevier)
Outcomes of the researchEstablished cooperation with two groups
Yeast Colony Group (YCG)
Department of Biology and Medical Genetics, Charles University in Prague - 2nd Faculty of Medicine (UBLG)
2 grant proposals:Image processing for microarrays
(GAČR, UTIA+YCG)
System for FISH analysis evaluation
(TAČR, UTIA+FIT+UBLG+CAMEA s.r.o.)
DNA microarray processing
14000 spots with red and green fluorescence(ratio of mRNA content of a given gene for two samples)
Image processing:
determination of exact location and size of the spot
elimination of the spots with strong background
Currently: high ratio of manual processing
FISH analysisFISH = Fluorescence In-Situ Hybridization
sample, containing DNA to be examined, is hybridized with a probe
probe: DNA fragment marked with a fluorescence dye
if the DNA sample contains complementary fragments,
the probe will match
this can be observed in a fluorescence microscope
the presence of signal indicates presence of a chromosome containing the DNA sequence
FISH analysisApplication
Detection of Turner syndrom (1 out of 2500 newborn girls)
Growth distortions, infertility,..
X monosomy in mosaic form with frequency <1% !
Detection of Klienefelter syndrome, etc. etc.
FISH analysis