1. MRI preprocessing
and
segmentation

2. Bias References

3. Segmentation References

4. Segmentation pipeline
Validation
Clarke, 1995

5. 1. Preprocessing 1.1. Brain extraction
1.2. Removal of field inhomogeneities (bias-field)

6. 1.1. Brain extraction
MRI of head Intracranial volume Extracted brain

7. 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 slices
correct the copied mask

8. 1.1. Brain extraction initial mask adjacent slice j mask of slice j
Huh, 2002
challenge

9. 1.1. Brain extraction
restoring truncated boundary

10. Let voxel have a value 1
if its intensity is higher than t
(determine t arbitrarily,
increase when needed)

11. 1.2. Removal of field inhomogeneities
Bias field
Phantom studies:
Typical signal falloff
in SI direction is 20%
intensity S I
20 % x

12. 1.2. Removal of field inhomogeneities
Statistical methods: probabilistic, gaussian and mixture models of bias-field
Polynomial methods: smooth polynomial fit to bias-field

13. 1.2. Removal of field inhomogeneities
Polynomial method example:
Milchenko, 2006

14. Milchenko, 2006

15. 1.2. Removal of field inhomogeneities
orig
model
bias
result
Shattuck, 2001

16. 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)

17. 3. Segmentation 3 tissue types: CSF, GM, WM 4 regions:
R1: air, scalp, fat, skull (background, removed)
R2: subarachnoid space (CSF)
R3: parenchyma (GM, WM)
R4: ventricles(CSF)

18. 3. Segmentation (dual echo:T2, PD or T1, T2, PD weighted)
(T1 weighted)
Clarke, 1995

19. 3. Segmentation T1 weighted, single intensity dual echo:T2, PD or
T1, T2, PD weighted or
T1 weighted
with feature vector
3.1. Histogram based
thresholding
3.2. Bayesian
Unsupervised
3.6. k-means fuzzy
cmeans
Supervised
Parametric Non-parametric ANN
3.3. Max. Likelihood k-NN MLP

21. 3.1. Histogram based thresholdingWM GM
Lcp crossing point
of tangents
Histogram of extracted, bias corrected brain in T1-weighted MRI
L = g * Lcp (set g manually on 80 images)
if I(x,y,z) < L then GM else WM
Schnack, 2001

23. 3.2. Bayesian segmentationWM
Population1
Population2
Population3
(#of voxels/#ofallvoxels in the brain)
(intensity)
Hypothetical distributions

24. 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:
setup graphs above
from regional data
GM, WM, CSF ratios
from volumetric studies
P(Tj ! I) = P(I ! Tj) . P(Tj)
Ξ P(I ! Tk). P(Tk) k
J,k=1,2,3:
1: CSF, 2: GM, 3:WM
Decide on tissue type m if:
P(Tm ! I) > P(Tj ! I) for all j
Kovacevic, 2002

25. 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

26. PD weighted
image
intensity
intensity
voxel x,y,z
T2 weighted
T2 weighted
image
Kovacevic, 2001

27. Suckling, 1999

28. 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
15 classes
Zavaljevski, 2000

29. 3.3. Maximum likelihood classifier
Zavaljevski, 2000
Normal subject Stroke patient

30. 3.4. K-NN, K-Nearest neighbor classifier
Hypothetical distribution
T2 intensity
T1 intensity
- 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

31. 3.4. K-NN classifier Uses 5 different contrast MRIs
manual atlas labels atlas labels
labels with linear reg with non-lin reg.
k= k=45
Vrooman, 2007

32. 3.5. ANN, MLP classifier for segmentation, :F M = 3, 3 classes MLP
Architecture:
1 layer:
linear contour
>1 layers:
complex contours
countours are
used for class
separation W1 W3
feature vector
transfer fcn: sigmoid

33. 3.5. ANN, MLP classifier
Results
This page is empty on purpose

34. 3.6. k-means classifier
This classifier is not used much in segmentation,
but explained here as an introduction to fuzzy c-means
Algorithm:
- k is equal to number of classes
- choose k arbitrary initial seed points (*)
- assume seed points are class centroids
1 for each sample point j,
find distance to all k centroids
Let j belong to class m if j is closest
to centroid m
2 for each class k, recalculate centroids
repeat steps 1 and 2 above until
no change in centroids
Note how class assignments change
at each iteration
Minimized measure:

35. 3.7. fuzzy c-means (FCM) classifier
k-means classifier FCM classifier
U: membership
row=each sample x
col=each class
minimized cost

36. 3.7. fuzzy c-means (FCM) classifier
Initialize U=[uij] matrix, U(0)
At k-step: calculate the centers vectors C(k)=[cj] with U(k)
initial
iteration 8
iteration 37
Update U(k) , U(k+1)
If || U(k+1) - U(k)||<
then STOP; otherwise return to step 2.

37. 3.7. fuzzy c-means classifier
Results

38. 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

39. 4. Validation Clark, 2006 Partial volume effect
for boundary separation
Shattuck, 2001
segmented
gold std
corrrect WM misclassified
(colored by subejct number
there are a total of 10 subjects)