ImageJ_bcm6013-E2011-J2- PL-MV-final

Transcription

Image processing and analysis
Monique Vasseur et Gabriel Lapointe
BCM6013 Summer 2011
Image Segmentation & Analysis
●
Image pre-processing
Background correction
Noise removal
Image Segmentation to distinct:
Objects from background
Objects between them
Image artefact corrections before analysis
Image Analysis
Morphologicial measurements
Quantification (Intensity, object classification in tables)
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Pre-processing
Background & noise removal
Image histogram
Subtraction of a constante (modal value)
Subtraction of background/noise images
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Black Level removal
Process > Math > Subtract ... (modal value)
LUT
Rainbow
Subtract
Modal
value
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Min:
Mode:
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187
©2011 Gabriel Lapointe Certains droits réservés.
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2
DIC Background correction
Process > FFT > Bandpass Filter...
1st step: correct the inequality of the background
Bandpass
Filter...
The background is homogeneous
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Segmentation
to isolate objects in an image
By criteria: intensity, color, shape…
Sometimes by knowledge (more suggestive)
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3
Segmentation approaches
Threshold: binarization of the image
Mask to retain only objects of interest
By regions: objects are defined by all its pixels
By contours: objects defined by its frontier pixels
* In fluorescence, thresholding is the most used
(imagery is mostly monochannel with immuno marquarge)
Thresholding
Binarisation of the histogram
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Thresholding
Image > Adjust > Threshold...
1 threshold : 550
selected < 550
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Thresholding
1 threshold : 550
selected < 550
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5
Thresholding
1 threshold : 1638
Selected > 1638
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Thresholding (Multiple thresholds)
2 threshold: 550 and 1561
550 > Selected < 1638
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Thresholding (2 thresholds)
2 threshold: 550 and 1561
550 > Selected < 1638
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Automatic thresholding
Automatic
thresholding
algorythmn
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7
Automatic thresholding
Li
Default
Huang
Intermodes
IsoData
MaxEntropy
Mean
MinError(i)
Minimum
Percentile
RenyiEntropy
Shanbhag
Moments
Otsu
Triangle
Yen
2010-11-12
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Nonuniform Threshold
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8
Sliding Threshold
Plugins > Particle Analysis > Sliding Threshold
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Segmentation by regions / contours
Binary morphological
operations
Erosion
Dilation
Open
Close
Fill holes
Watershed
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Erosion
Process > Binary > Erode
To reduce the size of objects, 1 or more pixels at a
time
Used primarily to remove small objects in the image
For big objects, to determine the internal contour
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Dilation
Process > Binary > Dilate
To increase the size of objects by 1 or more pixels at
a time
Mainly used to eliminate small holes in the object
For big objects, to determine the external contour
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10
Open
Process > Binary > Open
Erosion
Then
dilation
To eliminate small objects without affecting the size of the
largest
To separate 2 objects nearby
Softens the outlines of large objects
Equivalent to an erosion followed by dilation
CloseProcess > Binary > Close-
Dilation
Then
erosion
To “close” small holes in objects without affecting the size of
the objects
To reconnect jonctions
Softens the edges of large objects
Equivalent to a dilation followed by an erosion
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Fill holes
Process > Binary > Fill Holes
Fill Holes:
Fill the holes of all sizes without affecting the
periphery of objects
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Separation of object
Process > Binary > Watershed
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12
Watershed and irregular shapes
Watershed segmentation
does not work well on irregular shape objects
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Example
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Mask of whole cells
(1)
Threshold
Yen
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Mask of whole cells
(2)
Process > Binary > CloseOriginal
Threshold
Close-
Close the edge of the cell
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Mask of whole cells
(3)
Process > Binary > Open
Original
Threshold
Close-
Open
Some bright spots
in the background are eliminated
The holes within the cell are
eliminated
Mask of whole cells
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(4)
Analyze > Analyze Particles...
Original
Threshold
Close-
Open
Analyze Particles...
Some bright spots
in the background are eliminated
Small background debris are
excluded by their small size and
the holes are filled
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Mask of nuclei
Watershed
The nuclei with their regular shapes, are good candidates
for Watershed Segmentation
DIC Mask
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(2)
Process > Filter > Variance...
Original
Filtre BP
Variance...
Homogeneous background
We are looking for areas with
high intensity variations (edge
and organelles)
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DIC Mask
(3)
Original
Filtre BP
Variance
Threshold...
Otsu
« Edges »
Selected Interest
DIC Mask
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(4)
Process > Binary > Close-/Open
Original
Filtre BP
Variance
Threshold
Close-/Open
Selected Interest
Improved selection by removing
surrounding particles
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Combining masks
Cells
(1)
Transfected cells
Nuclei
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©2011Gabriel Lapointe Certains droits réservés.
Combining masks
(2)
Process > Image Calculator > … AND …
Keep only what is common
to two images
Transfected cells
Nuclei
AND
Nuclei of transfected cells
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Combining masks
(4)
Process > Image Calculator > … SUBTRACT …
Nuclei
Exclude from the first image what
is common with the second image
Transfected cells
Subtract
Nuclei of non transfected
cells
Combining masks
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(5)
Process > Image Calculator > … ADD …
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Exercises 4:
Cellules-3_*.tif
Make masks for each image (GFP and
DAPI)
Isolate the nuclei of cells transfected
with GFP
Save your final mask
Count the number of transfected cells
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Measurements and
analysis
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Analysis
Analyze > Measure...
Measure
• Measure the entire image or
• Measuring only a region if it is active
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Available informations
Analyze > Set Measurements...
Center-weighted intensity
Standard deviation
of intensity
The most frequent
intensity value
Center of selection
Replaces the
selection by an oval
Feret: Longest diameter
FeretAngle: angle (0-180 of ferret)
MinFeret: Smallest diameter
Sum of intensities
Distribution of 4th
=0 ; Normal (Gaussien)
<0 ; Flat
>0 ; acute
< -1.2 ; multimodal
Distribution of 3rd
=0 ; symetric
<0 ; asymetric left
>0 ; asymetric right
% of pixels above
threshold
Ne prendre en
compte
que les pixels au
dessus du Threshold
Take the measurments on a
different image, not the one
where we did the selection
The length and angle are also available if
selection is a line
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Length measurement
Image > Properties...
Analyze > Set Scale...
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Calibrate the pixel size using a
micrometer slide
1 square = 10 microns
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Calibrate the pixel size using a micrometer slide
Analyze > Set Scale...
Will be applied to all images
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Calibrate the pixel size using a micrometer slide
Image > Properties...
Before
After
Calibration
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Scale bar
Analyze > Tools > Scale Bar...
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Intensity profile
Analyze > Plot Profile
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3D Intensity profile
Analyze > Surface Plot...
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3D Intensity profile
Plugins > Interactive 3D Surface Plot
Representation of a 2D image into 3D
Intensity values
according to the coordinates (x, y)
Intensity curves (z)
according to the coordinates (x, y)
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Counting and analysis of object
Analyze > Analyze Particles...
Nothing
Outlines
●Masks
●Elipses
●Count masks
●
●
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Example: Measure the intensity of individual cells
Image > Adjust > Threshold
Threshold
Huang
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Process > Binary > Close- / Open
Threshold / Huang
Close- / Open
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Threshold
Close- / Open
Watershed
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Analyze > Analyze particles...
Threshold
Close- / Open
Watershed
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2
Analyze particles...
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Show : Outlines
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5
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Analyze > Analyze particles... (redirect to: Original)
Threshold
Close- / Open
Watershed
250
200
1
2
1
Redirect to: Original
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150
2
3
4
100
5
Intensity (%)
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Black Level
50
5
0
Mean
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L’influence du niveau de noir
Black level
Threshold
Close- / Open
Watershed
400
350
1
2
300
1
250
Redirect to: Original
2
200
3
4
150
5
Intensity (%)
3
100
4
Black Level
50
0
5
Mean
Corrected mean
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Example: Count the number of stress granules and
measure the average area
Analyze > Measure (Mode) + Process > Substract
Remember to calibrate the size of the pixels before you start!
Subtract
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Select the smallest granules (low intensity + size); Analyze > Measure
Subtract
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Subtract
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Sliding Threshold
Minimal intensity of the
granules
Maximum intensity of the
image
Intensity variation
between the granule and
the background
Minimal diameter of the
granules
Maximal diameter of the
granules
Circularity Min and Max
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30
Substract
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Sliding Threshold
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Binary operations binaires and Analyze particules
Substract
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Sliding Threshold
Open
Close
Watershed
Analyze Particles
85 granules
Mean of 2,3 µm2
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Other interesting
applications!
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Quantification of bands
1) The box method
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Quantification of bands
2) Indirect selection
By using the option in Redirect
To : setMeasurments can
select a band in the mask and
give measures of the original
image
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Afternoon workshop (2)
Work on your images
Count the number of stress granules
and get their average size (area, Feret
diameter)
Count the average number of
granules per cell
Questions, problems ... Ask us, we're
here for you!!
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ImageJ_bcm6013-E2011-J2- PL-MV-final

Transcription

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