Image processing for selected biological experiments J. Schier, B. Kovář ÚTIA AV ČR, v.v.i

Image processing for selected biological experiments

J. Schier, B. Kovář

ÚTIA AV ČR, v.v.i.

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Contents

Counting of yeast colonies(Future projects):

Microarray scans

Images from the FISH analysis (Fluorescence In-Situ Hybridization)

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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

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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

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Results

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# 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

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#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

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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

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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