Segmentation of Brain Tissues on MR Images Using Data Driven Techniques - PowerPoint PPT Presentation

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Segmentation of Brain Tissues on MR Images Using Data Driven Techniques

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Segmentation of Brain Tissues on MR Images Using Data Driven Techniques Ali Hojjat1, Judit Sovago2, Alan Colchester1 1University of Kent, Canterbury – PowerPoint PPT presentation

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Title: Segmentation of Brain Tissues on MR Images Using Data Driven Techniques


1
Segmentation of Brain Tissues on MR ImagesUsing
Data Driven Techniques
  • Ali Hojjat1, Judit Sovago2, Alan Colchester1
  • 1University of Kent, Canterbury
  • 2Karolinska Institute, Stockholm
  • PVEO Project Meeting
  • Budapest, 11-12 June 2004

2
Classification of segmentation techniques
  • Segmentation techniques
  • Design based on different principles, eg.
  • Edge based- Use local changes (gradient)
    Problem Very sensitive to noise
  • Region based- Use homogeneity to segment the
    image
  • Region growing techniques Problem of
    overgrowing
  • Statistical analysis in feature space like
    histogram Problem lack of spatial information
  • Combined region and edge based techniques
  • Atlas Based- Geometric model of the image
    Problem Relies on the performance of the
    co-registration step.
  • Hybrid techniques- Combination of two or more of
    the segmentation techniques.

3
MR acquisition problems affecting segmentation
  • Noise
  • Spatial resolution and partial volume effect
  • Low resolution
  • Anisotropic sampling
  • Intensity inhomogeneity
  • Motion artefacts

4
Evaluation of segmentation techniques
  • Evaluating complete systems in an application
    domain requires proper definition of application
    goals and of criteria by which performance is to
    be judged
  • Evaluation of algorithms in isolation is
    difficult
  • Gold standard usually not available
  • For specific task we might need more than one
    segmentation algorithm to cover the wide
    categories of images.

5
Segmentation techniquesused
6
SPM
  1. Co-register the image with an atlas using an
    affine transformation.
  2. Perform Cluster Analysis with a modified Mixture
    Model and a-priori information about the
    likelihoods of each voxel being one of a number
    of different tissue types.
  3. Do a "cleanup" of the partitions
  4. Write the segmented images. The names of these
    images have "_seg1", "_seg2" "_seg3" appended
    to the name of the first image passed.

7
Growing process
  • The grey values of successive
  • points joining the region are
  • shown bellow

8
Some definitions
  • A region containing 20 pixels (shown in black and
    green).

External boundary, EB
  • External boundary (EB) regions boundary
  • Internal boundary (IB) outermost pixels
    inside the region
  • Peripheral contrast (Mean IB) / (Mean EB)

9
  • Grey level (top) and PC (bottom) mapping when
    region growing started inside the scalp.

10
Segmentation of scalp and skull base
11
Flowchart Segmentation of scalp
Original T1 MRI
Seed point In scalp
Region growing
Find maximum Peripheral contrast
Segmented scalp
12
  • Grey level (top) and peripheral contrast (bottom)
  • during the growing process for two conditions,
    with
  • and without the mask.

13
Sample MR image
  • MR image of an elderly patient with cerebral
    atrophy.
  • Segmented WM, GM and CSF are shown in
  • Grey, dark grey and white, respectively.

14
Sample MR image
15
3-D Visualisation of the brain
  • Left

Right
16
BrainSeg Summary
  • It needs manually selected seed points in the
    scalp and in the brain
  • Works well on abnormal as well as normal images
  • Speed of segmentation of every structure is about
    3-4 minutes
  • Total speed would be about 10 minutes. The speed
    may vary according to the resolution of the input
    image and the level of artefacts.

17
Evaluation of the segmentation techniques
18
Practical issues related to manual segmentation
  • Questions
  • Should we use anatomical knowledge to segment a
    region or only rely on intensity values?
  • What is the best decision when the distance
    between two parts of a sulcus is very close to
    zero (touching each other, or less than one
    pixel)? Crack between pixels might be a good
    idea, but we should go to subpixel level.
  • Should we rely mainly on our anatomical knowledge
    when partial volume affects the intensity?

19
  • Coordinates (104,139,80)

20
RG
BrainSeg
SPM
21
Performance of automatic segmentation technique
for brain (WM GM) tissue
BrainSeg
22
Rate of falsely detected voxels in every slice
for the two segmentation techniques
BrainSeg
23
SPM
Voxels which are missed by both techniques are
shown in green
  • RG

BrainSeg
24
Voxels which are falsely segmented by the two
techniques are shown in red
SPM
RG
BrainSeg
25
Issues related to segmentation of WM
  • WM segmentation is more difficult.
  • Boundary between WM and GM is not clear.
  • Segmenting tissues changed from their original
    form,like WM lesions, is difficult. Should we
    segment the WM lesions as WM, GM or
    separate tissue?

26
Performance of automatic segmentation technique
for WM tissue
BrainSeg
27
Rate of falsely detected voxels in every slice
for the two segmentation technique
BrainSeg
28
False positive points (in orange) in the WM
overlaid on manual segmentation
Original image
Manual
SPM
29
False positive (in green) and missed points (in
purple) in whole brain
Original image
Manual
30
Summary
  1. Manual segmentation of images is very difficult.
  2. Performance of SPM and BrainSeg techniques are
    very similar.
  3. SPM can segment GM structures (in basal ganglia)
    better than BrainSeg.
  4. BrainSeg performs better around the cortex.
  5. BrainSeg is three times faster than SPM.
  6. Evaluation should be extended to a larger number
    of subjects.
  7. We plan to compare the results of PVE correction
    using different techniques against manual
    segmentation.

31
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