1 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Cédric Dufour (...

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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