20080125 friday food

19/03/2007Quantitative analysis of ultrasound images of the preterm brain

Quantitative analysis of ultrasound images of the preterm brain

Ewout VansteenkisteIBBT-Medisip/IPI-UGENTFriday Food 25/01/2008


Outline

[Source: William Lawson, A new Orchard and Garden, 1648, Londen]

quantitative image analysis

medicalultrasound

texture-classification

psycho-physics segmentation

registration

speckle-reduction in ultrasound

2D echo/3D MRIregistration

white matterclassification

white mattersegmentation

ventriclesegmentation

segmentationcarotid

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Quantitative image analysis

tumor = “white dot” in the imagesize = “small”, “average”

Qualitative analysis : in words

2.25 cm²

Both experts measure the tumorUsing the same segmentation

algorithm

Quantitative analysis: through measuring

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Medical ultrasound

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0

50

100

150

200

250

SPECKLE

probe

electric current

Piëzo-electric cristal pulsing

Tissue structures/transitionsskin


White matter damage = Periventricular Leukomalacia (PVL)

grey matter: what we think with

white matter: highways of the brain

ventricles: cavities regulating brain fluid

- Preterm infants born between week 26 and 32

- Very low birth weight (<1500g)

- Lack of oxygen = increased chance on brain damage

mild

severe

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White matter damage diagnosis

- critical condition preterms: incubated/ventilated- acquiring high-quality scans (MRI) is not trivial in first days of life (starts only at day 3)- fontanelle open = early echo is possible - early detection is important

pros: - non-invasive/safe - portable

- real-time imaging- relatively cheap

contras: - poor image quality- diagnosis = subjective

“flaring”

normal severe mild

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flaring


Problem = subjectivity

- Flaring (areas) difficult to describe - Not easy to delineate objectively- Qualitative diagnosis insufficient

- Quantitative objective results? 1) flaring? 2) how widely spread?

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Tissue texture classification (1)

- Texture: no unique definition. Description: regular, irregular, stochastic pattern present in most natural scenes:

- Texture parameters: mathematical measures expressing texture characteristics

- Scale determines texture:

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- Important for ultrasound: tissue structure is manifested as speckle texture


Example texture parameters: co-occurrence matriceswood cloth

Contrast = 100Entropy = 0.78


pathological benign



255

0 255

0

2D co-occurrence matrix

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length

w

idth

length

w

idth

length

wid

th

??

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patholigical non-pathological

140 patients


Machine-settings:

-Gain-Power -Time Gain Compensation

Compensation algorithm

Quantitative analysis = precision 92.5%

= sensitivity 88%

Texture parameters:- Skewness- Contrast- Angular second moment- X-gradient- Y-gradient

Bayes classifier

KNN classifier

classifier combination

- pathological- non-pathological

Normalization

LDAclassifier

Qualitative analysis = precision 75%

= sensitivity 70%

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Outline


Quantitative image analysis

medicalultrasound

Texture classification

white matterclassification

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Flare segmentation and area estimation

Validation?

Initial texture- Basd segmen-Tation map

-Morfological closing-Gradient-Opening byReconstruction

expert existingnew

Expert delineation: subjective?2D US 3D MRIregistrati

on

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sensitivity 98%


Multimodal image registration

- idea = “1 + 1 adds up to more than two”- registering = aligning images so that relevant structures overlap- multimodal = images of different image modalities, CT, MRI, echo- fusion = the result after registration

Magnetic Resonance Scanner + 3D high-quality imaging- limited scan time resulting in low axial-resolution

+ 2D high-resolution- low image quality

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Multimodal 2D ultrasound to 3D MRI registration

initialization

Mutuel InformationMetric

Regular StepGradientDescent

Rigid Transformation

Trilinear Interpolation

result

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Validation registration = “CAVE” + segmentation

Registration algorithm

flaring segmentation MRI-flaring expert

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Segmentation extended: ventricles + carotid

enlarged ventriclesindicative for PVL

3D reconstruction

2D segmentation

Bifurcation of the carotid: atherosclerosis

3D reconstruction

[Source = Glor, 2004]

2D segmentation

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Outline


quantitative image analysis

medicalultrasound

Texture-classification

segmentation

registration

2D echo/3D MRIregistration

white matterclassificaton

white mattersegmentation

ventriclesegmentation

segmentationcarothid

17/24


Psychovisual experiments

STIMULISUBJECTS

dummy? physicians?

Experts?

METHODOLOGY

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Image degradations

- Increasing amount of artefact removing algorithms:

- Problem: how to assess the quality of degraded/filtered images

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blur noise artefacts


Set-up experiment: filtering of ultrasound images

- Can we facilitate a qualitative diagnosis by speckle suppression?

- GenLik-methode developed at our department adapted to ultrasound images

- Improved technique applied successfully on quantitative registration and segmentation tasks

[bron: Pizurica, 2002]

original filtered

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methodology + results

- Different scenes (stimuli) were selected containing diverse image structures and 4 levels of filtering:

- Given the difficulty to differentiate noise from relevant information, following question was posed to the experts involved in the experiment :

- Which image do you prefer in terms of diagnostic image quality (discrete scale -3 to 3)

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Les

suite

d fo

r di

agn

osis

degree of filtering 0 1 2 3 4

speckle reductionrejected !


Conclusion

-Classification algorithm for white matterdamage

-Segmentation algorithm to estimate flaring area (in practice since 2005)

-Registration algorithm to align 2D ultrasound images and 3D MRI data

-Ventricle segmentation-Segmentation algorithm carotid

-Psycho-visual experiment on speckle suppression

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Improved, early quantitativedetection of PVLsensitivity: 70% 98%+ultrasound moresensitive than MRI


and remember…

The more questions you now ask, the less time we have to eat…

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20080125 friday food

Documents

white matter damage

white dot

brain fluid

brain ventricles

image size

preterm infants

expertsmeasurethe tumor

new orchard