Segmentation of Brain Tissues on MR Images

Using Data Driven Techniques

Ali Hojjat1, Judit Sovago2, Alan Colchester1 1University of Kent, Canterbury 2Karolinska Institute, Stockholm

PVEO Project Meeting

Budapest, 11-12 June 2004

Classification of segmentation techniques

Segmentation techniquesDesign 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.

MR acquisition problems affecting segmentation

• Noise• Spatial resolution and partial volume effect

– Low resolution – Anisotropic sampling

• Intensity inhomogeneity• Motion artefacts

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.

Segmentation techniquesused

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.

Growing process:

The grey values of successive points joining the region are shown bellow:

Pixel Number (in order of linking)

Some definitions:

A region containing 20 pixels (shown in black and green).

• External boundary (EB) = regions boundary• Internal boundary (IB) = outermost pixels inside

the region • Peripheral contrast = (Mean IB) / (Mean EB)

External boundary, EB

Grey level (top) and PC (bottom) mapping when region growing started inside

the scalp.

Segmentation of scalp and skull base

Flowchart: Segmentation of scalp

Original T1

MRI

Seed point

In scalpRegion growing

Find maximum

Peripheral contrast

Segmented

scalp

Grey level (top) and peripheral contrast (bottom) during the growing process for two conditions, with and without the mask.

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.

Sample MR image

3-D Visualisation of the brain

Left Right

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.

Evaluation of the segmentation techniques

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?

Coordinates (104,139,80)

RG SPMBrainSeg

Whole brain segmentation result (True Positive rate)

0.80

0.82

0.84

0.86

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1.00

Slice No

SPM

Region Growing

Performance of automatic segmentation technique for brain (WM + GM) tissue

BrainSeg

Falsely segmented voxels in whole brain (Fase Positive rate)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 4 7

10

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Slice No

Err

or

rate

SPM

Region Growing

Rate of falsely detected voxels in every slice for the two segmentation techniques

BrainSeg

Voxels which are missed by both techniques are shown in green

SPM

RGBrainSeg

SPM

RG

Voxels which are falsely segmented by the two techniques are shown in red

BrainSeg

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?

WM segmentation result

0.950

0.955

0.960

0.965

0.970

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0.995

1.000

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

Slice No

SPM

Region Growing

Performance of automatic segmentation technique for WM tissue

BrainSeg

WM segmentation, Falsely segmented voxels

0.00

0.05

0.10

0.15

0.20

0.25

0.30

80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

Slice No

Eo

rro

r ra

te

SPM

Region Growing

Rate of falsely detected voxels in every slice for the two segmentation technique

BrainSeg

BrainSeg SPM

False positive points (in orange) in the WM overlaid on manual segmentation

Original

image

Manual

BrainSeg –FP & MPSPM -FP & MP

False positive (in green) and missed points (in purple) in whole brain

ManualOriginal

image

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.