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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Cédric Dufour
( LTS-IBCM Collaboration )
The ‘The ‘microtubulesmicrotubules’ project’ project
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
The ‘The ‘microtubulesmicrotubules’ project’ project Morphological filteringMorphological filtering Markers extractionMarkers extraction Microtubules extractionMicrotubules extraction ResultsResults Algorithm testingAlgorithm testing Final assessmentFinal assessment
Content
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Obtain Obtain specific proteins’ density statisticsspecific proteins’ density statistics, related to , related to the neural cell microtubules structures.the neural cell microtubules structures.
Markers
The ‘microtubules’ project The goal
Microtubules
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
The ‘microtubules’ project The various steps
Isolate the markers Isolate the markers maskmask..
Isolate the microtubules Isolate the microtubules mask mask andand skeleton skeleton..
Compute the microtubules’ surface and length.Compute the microtubules’ surface and length.
Compute the markers’ quantity, overall and near the Compute the markers’ quantity, overall and near the
microtubules.microtubules.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
The ‘microtubules’ project The way it is done
Markers extraction :Markers extraction :
Morphological filtering and local maximum Morphological filtering and local maximum
detection.detection.
Microtubules extraction :Microtubules extraction :
Selective filtering using linear oriented structuring Selective filtering using linear oriented structuring
element’s correlation with thresholded image.element’s correlation with thresholded image.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Morphological filtering What is it ?
Morphological filteringMorphological filtering is a filtering method is a filtering method
originated from the theory of originated from the theory of mathematical mathematical
morphologymorphology..
The base of all morphological processing are the The base of all morphological processing are the
‘‘erosionerosion’ and ‘’ and ‘dilationdilation’ morphological functions.’ morphological functions.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Morphological filtering Binary ‘erosion’
Bb
B bXXbyByyX
,,
Original set Eroded set
Structuring elmt.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Morphological filtering Binary ‘dilation’
Bb
B bXBbXxbxX
,,
Original set Dilated set
Structuring elmt.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Morphological filtering Binary ‘opening’
XX BBB
Original set Opened set
Structuring elmt.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Morphological filtering Binary ‘closing’
XX BBB
Original set Closed set
Structuring elmt.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Replace the Replace the intersectionintersection//unionunion operators with operators with infimuminfimum//supremumsupremum operators operators
yxfxf
yxfxf
ByB
ByB
sup
inf
Morphological filtering Function (or ‘gray scale’) morphology
Original fct.
Dilated fct.
Eroded fct.
Original fct.
Closed fct.
Opened fct.
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Base imageBase image
Markers extraction Step by step : 1
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Opening with a 3x3 squareOpening with a 3x3 square
Markers extraction Step by step : 2
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Subtraction with original image (Tophat)Subtraction with original image (Tophat)
Markers extraction Step by step : 3
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
NormalizationNormalization
Markers extraction Step by step : 4
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
‘‘Loosy’ thresholdLoosy’ threshold
Markers extraction Step by step : 5
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Remove small elements (artefacts)Remove small elements (artefacts)
Markers extraction Step by step : 6
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Mask base image ( and normalize )Mask base image ( and normalize )
Markers extraction Step by step : 7
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Local maximum detection ( in 5x5 disc neighborhood )Local maximum detection ( in 5x5 disc neighborhood )
Markers extraction Step by step : 8
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Resulting Resulting markers maskmarkers mask
Markers extraction In the end
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Base imageBase image
Microtubules extraction Step by step : 1
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Opening with 21x21 square (Opening with 21x21 square ( background) background)
Microtubules extraction Step by step : 2
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Subtraction with original image (Tophat)Subtraction with original image (Tophat)
Microtubules extraction Step by step : 3
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Markers removalMarkers removal
Microtubules extraction Step by step : 4
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
ThresholdThreshold
Microtubules extraction Step by step : 5
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Resulting binary imageResulting binary image
Microtubules extraction Step by step : 6
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Oriented linear element correlationOriented linear element correlation
Microtubules extraction Step by step : 7
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Small elements (artefacts) removalSmall elements (artefacts) removal
Microtubules extraction Step by step : 8
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Repeat last 2 steps for different orientationsRepeat last 2 steps for different orientations
Microtubules extraction Step by step : 9
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Thresholding of filter accumulatorThresholding of filter accumulator
Microtubules extraction Step by step : 10
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Closing with a 3x3 cross to remove irregularitiesClosing with a 3x3 cross to remove irregularities
Microtubules extraction Step by step : 11
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Skeleton by thinning and cntd. points suppressingSkeleton by thinning and cntd. points suppressing
Microtubules extraction Step by step : 12
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Resulting Resulting microtubules mask microtubules mask andand skeleton skeleton
Microtubules extraction In the end
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Results Image 1 (demo image)
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Results Image 2 (low density)
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Results Image 3 (high density)
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Errors Errors markers: 0% / 4.8% markers: 0% / 4.8%
Algorithm testing Real image (PSNR = 35dB)
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Errors Errors microtubules: 0.7% / 0.5%; markers: 0% / 0% microtubules: 0.7% / 0.5%; markers: 0% / 0%
Algorithm testing Synthetic image (PSNR = 35dB)
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
We’ve been able to offer the biologists…We’ve been able to offer the biologists…
– a fully automatic analysis program,
– running in a powerful and wide-spread environment
(MatLab),
– giving good results, according to the biologists’ needs.
Final assessment In general
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Microtubules mask extraction :Microtubules mask extraction :
The poor quality of the input images (very low The poor quality of the input images (very low
contrast) leads to a low-efficiency microtubules contrast) leads to a low-efficiency microtubules
mask extraction procedure (mask extraction procedure (the algorithm misses the algorithm misses
the most evanescent structuresthe most evanescent structures).).
Final assessment Problems
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Microtubules mask extraction :Microtubules mask extraction :
Use tracking algorithm to follow the full microtubule Use tracking algorithm to follow the full microtubule
(pseudo-linear) structure.(pseudo-linear) structure.
N.B.N.B. This is not easy because of the variable This is not easy because of the variable
number of microtubules that may cross in one point number of microtubules that may cross in one point
(resulting in tracking uncertainties)(resulting in tracking uncertainties)
Final assessment Improvements
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Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
Thank you for your attentionThank you for your attention