1 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Cédric Dufour ( LTS-IBCM Collaboration ) The ‘microtubules’ project

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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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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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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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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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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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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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Morphological filtering Binary ‘erosion’

Bb

B bXXbyByyX

,,

Original set Eroded set

Structuring elmt.

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Morphological filtering Binary ‘dilation’

Bb

B bXBbXxbxX

,,

Original set Dilated set

Structuring elmt.

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Morphological filtering Binary ‘opening’

XX BBB

Original set Opened set

Structuring elmt.

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Morphological filtering Binary ‘closing’

XX BBB

Original set Closed set

Structuring elmt.

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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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Base imageBase image

Markers extraction Step by step : 1

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Opening with a 3x3 squareOpening with a 3x3 square


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Subtraction with original image (Tophat)Subtraction with original image (Tophat)


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NormalizationNormalization


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‘‘Loosy’ thresholdLoosy’ threshold


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Remove small elements (artefacts)Remove small elements (artefacts)


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Mask base image ( and normalize )Mask base image ( and normalize )


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Local maximum detection ( in 5x5 disc neighborhood )Local maximum detection ( in 5x5 disc neighborhood )


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Resulting Resulting markers maskmarkers mask

Markers extraction In the end

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Base imageBase image

Microtubules extraction Step by step : 1

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Opening with 21x21 square (Opening with 21x21 square ( background) background)


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Subtraction with original image (Tophat)Subtraction with original image (Tophat)


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Markers removalMarkers removal


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ThresholdThreshold


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Resulting binary imageResulting binary image


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Oriented linear element correlationOriented linear element correlation


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Small elements (artefacts) removalSmall elements (artefacts) removal


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Repeat last 2 steps for different orientationsRepeat last 2 steps for different orientations


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Thresholding of filter accumulatorThresholding of filter accumulator


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Closing with a 3x3 cross to remove irregularitiesClosing with a 3x3 cross to remove irregularities


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Skeleton by thinning and cntd. points suppressingSkeleton by thinning and cntd. points suppressing


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Resulting Resulting microtubules mask microtubules mask andand skeleton skeleton

Microtubules extraction In the end

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Results Image 1 (demo image)

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Results Image 2 (low density)

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Results Image 3 (high density)

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Errors Errors markers: 0% / 4.8% markers: 0% / 4.8%

Algorithm testing Real image (PSNR = 35dB)

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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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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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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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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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Thank you for your attentionThank you for your attention

Documents

1 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Cédric Dufour ( LTS-IBCM Collaboration ) The ‘microtubules’ project