mri preprocessing and segmentation
DESCRIPTION
MRI preprocessing and segmentation. Bias References. Segmentation References. Validation. Segmentation pipeline. Clarke, 1995. 1. Preprocessing 1.1. Brain extraction 1.2. Removal of field inhomogeneities (bias-field). 1.1. Brain extraction. - PowerPoint PPT PresentationTRANSCRIPT
MRI preprocessing and
segmentation
Bias References
Segmentation References
Segmentation pipeline
Clarke, 1995
Validation
1. Preprocessing
1.1. Brain extraction
1.2. Removal of field inhomogeneities (bias-field)
1.1. Brain extraction
MRI of head Intracranial volume Extracted brain
1.1. Brain extraction
FSL: Initiate a mesh inside the skull and expand-wrap onto brain surface
Huh, 2002 method: go to mid sagittal, find brain, copy mask on adjacent slicescorrect the copied mask
1.1. Brain extraction
initial mask adjacent slice j mask of slice j
challengeHuh, 2002
1.1. Brain extraction
restoring truncated boundary
Let voxel have a value 1 if its intensity is higher than t
(determine t arbitrarily,increase when needed)
1.2. Removal of field inhomogeneities
Bias field
Phantom studies:Typical signal falloff in SI direction is 20%
S
I20 %
x
intensity
1.2. Removal of field inhomogeneities
Statistical methods: probabilistic, gaussian and mixture models of bias-field
Polynomial methods: smooth polynomial fit to bias-field
1.2. Removal of field inhomogeneitiesPolynomial method example:
Milchenko, 2006
Milchenko, 2006
1.2. Removal of field inhomogeneities
Shattuck, 2001
orig model
bias result
2. Feature extraction
Features:- Intensities in a single MRI: univariate classification
- Feature vector from a single MRI: multi-variate class.ex: [I(x,y,z) f(N(x,y,z)) g(N(x,y,z))]
where N : neighbourhood around (x,y,z) f: distribution of I in neighborhood (entropy) g: average I in neighborhood or f, g specify edge or boundary information
- Intensities in multiple MRIs with different contrast: multi-variate (multi-spectral)
3. Segmentation
4 regions:R1: air, scalp, fat, skull (background, removed)R2: subarachnoid space (CSF)R3: parenchyma (GM, WM)R4: ventricles(CSF)
3 tissue types:CSF, GM, WM
3. Segmentation
Clarke, 1995
(T1 weighted)(dual echo:T2, PD or T1, T2, PD weighted)
3. Segmentation
T1 weighted, single intensity dual echo:T2, PD or T1, T2, PD weighted
or T1 weighted
with feature vector3.1. Histogram based
thresholding
Unsupervised
3.6. k-means 3.7. fuzzy cmeans
Supervised
Parametric Non-parametric ANN
3.3. Max. Likelihood 3.4. k-NN 3.5. MLP
3.2. Bayesian
3.1. Histogram based thresholding
Schnack, 2001
WM
GM
Histogram of extracted, bias corrected brain in T1-weighted MRI
Lcp crossing point of tangents
L = g * Lcp (set g manually on 80 images)if I(x,y,z) < L then GM else WM
Population1
Population2
Population3
3.2. Bayesian segmentation
WMGM
Hypothetical distributions
(intensity)
(#of voxels/#ofallvoxels in the brain)
3.2. Bayes’ classifier
For each voxel, x,y,z:Assume K tissue types (for eg. T1, T2, ..., Tk) possible, for 1 observed intensity, I:
P(Tj ! I) = P(I ! Tj) . P(Tj)
Ξ P(I ! Tk). P(Tk) k
GM, WM, CSF ratiosfrom volumetric studies
setup graphs above from regional data
Decide on tissue type m if: P(Tm ! I) > P(Tj ! I) for all j
Kovacevic, 2002
J,k=1,2,3:1: CSF, 2: GM, 3:WM
Methods based on feature vector or multi-spectral data
Supervised vs unsupervised Methods
Supervised: - Color indicates known classes - Separation contour is to be found during training phase- Separation contour is used for classification during recall phase
Unsupervised: - No color, classes unknown- Clusters are found during training phase- Association with clusters are made during recall phase
Kovacevic, 2001
T2 weightedvoxel x,y,z
PD weightedimage
T2 weightedimage
intensity
intensity
Suckling, 1999
3.3. Maximum likelihood classifier
- Assume the distribution P(I ! Tj) in Bayes can be obtained by a mixture of Gaussian or Normal distribution- Estimate means and co-variance matrix- For better results use Hidden Markov fields within neighborhoods
Zavaljevski, 2000
15 classes
3.3. Maximum likelihood classifier
Zavaljevski, 2000
Normal subject Stroke patient
3.4. K-NN, K-Nearest neighbor classifier
T1 intensity
T2 intensity
Hypothetical distribution
- k is always odd, 1<k<15 (as k increases comput time increases)- given a point p find k closest samples known from before- decide on class m where m is the highest number of classes among these k samples
3.4. K-NN classifier
k=1 k=45
manual atlas labels atlas labels labels with linear reg. with non-lin reg.
Vrooman, 2007
Uses 5 different contrast MRIs
MLPArchitecture:1 layer: linear contour
>1 layers: complex contours
countours areused for classseparation
transfer fcn: sigmoid
W1 W3
:F
3.5. ANN, MLP classifier
for segmentation,M = 3, 3 classes
feature vector
3.5. ANN, MLP classifier
Results
This page is empty on purpose
3.6. k-means classifier
Algorithm:- k is equal to number of classes- choose k arbitrary initial seed points (*)- assume seed points are class centroids1 for each sample point j, find distance to all k centroids Let j belong to class m if j is closest to centroid m2 for each class k, recalculate centroids
repeat steps 1 and 2 above until no change in centroids
Note how class assignments changeat each iteration
Minimized measure:
This classifier is not used much in segmentation, but explained here as an introduction to fuzzy c-means
3.7. fuzzy c-means (FCM) classifier
k-means classifier FCM classifier
U: membership row=each sample xcol=each class
minimized cost
3.7. fuzzy c-means (FCM) classifier
initial
iteration 8
iteration 37
Initialize U=[uij] matrix, U(0)
At k-step: calculate the centers vectors C(k)=[cj] with U(k)
Update U(k) , U(k+1)
If || U(k+1) - U(k)||< then STOP; otherwise return to step 2.
3.7. fuzzy c-means classifier
Results
4. Validation
Important issues:
- Partial volume effect, visualization
- Validation in manually segmented image
- Performance comparison with other methods on simulated image: Ex: Brainweb from Mcgill
4. Validation
Partial volume effectfor boundary separationShattuck, 2001
corrrect WM misclassified(colored by subejct number
there are a total of 10 subjects)
segmentedgold std
Clark, 2006