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 2Karolinska Institute, Stockholm
PVEO Project Meeting
Budapest, 11-12 June 2004
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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.
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MR acquisition problems affecting segmentation
• Noise• Spatial resolution and partial volume effect
– Low resolution – Anisotropic sampling
• Intensity inhomogeneity• Motion artefacts
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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.
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Segmentation techniquesused
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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.
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Growing process:
The grey values of successive points joining the region are shown bellow:
Pixel Number (in order of linking)
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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
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Grey level (top) and PC (bottom) mapping when region growing started inside
the scalp.
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Segmentation of scalp and skull base
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Flowchart: Segmentation of scalp
Original T1
MRI
Seed point
In scalpRegion growing
Find maximum
Peripheral contrast
Segmented
scalp
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Grey level (top) and peripheral contrast (bottom) during the growing process for two conditions, with and without the mask.
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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.
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Sample MR image
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3-D Visualisation of the brain
Left Right
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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.
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Evaluation of the segmentation techniques
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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?
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Coordinates (104,139,80)
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RG SPMBrainSeg
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Whole brain segmentation result (True Positive rate)
0.80
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1.00
Slice No
SPM
Region Growing
Performance of automatic segmentation technique for brain (WM + GM) tissue
BrainSeg
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Falsely segmented voxels in whole brain (Fase Positive rate)
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0.05
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0.35
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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
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Voxels which are missed by both techniques are shown in green
SPM
RGBrainSeg
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SPM
RG
Voxels which are falsely segmented by the two techniques are shown in red
BrainSeg
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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?
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WM segmentation result
0.950
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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
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WM segmentation, Falsely segmented voxels
0.00
0.05
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0.15
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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
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BrainSeg SPM
False positive points (in orange) in the WM overlaid on manual segmentation
Original
image
Manual
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BrainSeg –FP & MPSPM -FP & MP
False positive (in green) and missed points (in purple) in whole brain
ManualOriginal
image
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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.
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