Local Adaptive Segmentation
Robert Dahnke, Gabriel Ziegler, Christian Gaser
Structural Brain Mapping Group, Department of PsychiatryFriedrich-Schiller-University of Jena, Germany

INTRODUCTIONRESULTS

Segmentation of brain tissue into white matter (WM), gray matter(GM) and cerebrospinal fluid (CSF) is an important issue forstudying cortical morphology and brain functions (Ashburner,2005). After removing the noise and inhomogeneity, a Gaussian
mixture model allows the tissue segmentation (Ashburner, 2000).
However, even if the nonuniformity was removed completely,
the tissue-contrast can be very poor. GM regions with high ironconcentration, like the motor cortex and the occipital regions(Haacke, 2005), often have increased intensities that lead tomisclassifications.
Here we present a new method that allows adaption of local
intensity differences. Since these alterations happen relatively
smooth within a tissue class, a region growing approach canbe used to identify the segments more correctly. The extracted
intensities afford corrections of the local image intensity.

For validation the Collins phantom (Collins et. al. 1998) withdifferent noise level and inhomogeneity was used (Table1). Because the phantom based on a segmentation and has
thereby a good GM-WM contrast, the results are limited to test
segmentation stability (Table 1). Besides this we used the IBSR2
dataset (http://www.cma.mgh.harvard.edu/ibsr/) to compare themethod for more realistic conditions. Further single MR scans
without ground truth segmentation were used for visual tests.

METHODS
Input is a noise- and bias-corrected image (Figure 1a), and a brainmask (Dahnke et al., 2011). We start with clearly classifiable regionsthat are described by low gradients and class specific intensities
that are estimated by a Gaussian mixture model (Figure 1b).
Then, a region growing that aligns voxel with similar intensity isused to dilate each class. A maximum distance and the specificintensities of other classes limit the region growing (Figure 1c).
Nevertheless, it is possible that two classes align the same voxel.
Because most neighborhoods contain class specific voxels, a small
overlap is unproblematic and still allows a clear separation. We
choose a 5x5x5 voxel box neighborhood.
After finding the major tissue segments, we can estimate the local
intensity of each class voxel by averaging voxels of the same classwithin its neighborhood. To remove outliers only voxels within a SD
below 80% of the maximum SD of the neighborhood are used.
As a result, each class voxel now contains the local intensity ofits class (Figure 1d). To transfer these values to other non-classvoxels, i.e. to get the GM value for a WM voxel, we estimate thehull of each class by morphological operations and set all outervoxels to the median class intensity. A Laplace filter with Dirichletboundary condition is used to estimate the local intensity for all
non-class voxels within the hull (Figure 1e). To avoid hard edges
each class image is smoothed (Figure 1f). Finally, the input image
T is scaled by the class maps, resulting in a map TSEG, with WM=3,GM=2, CSF=1, and background=0 (Figure 1g). After a second noisecorrection a CSF-GM-WM tissue segmentation with a MRF filter
is used to create the final segmentation (Figure 2).
Calculation times with skull stripping, bias- and noise-correction
take 4 minutes without, and 5 minutes with local adaption at
1mm resolution.
a) bias-corrected T1b) initial segments

datasetmethodSPM8
VBM8FSL
our method

kappa(CSF)
0.7683±0.0326
0.7487±0.0213
0.7281±0.0507
0.7491±0.0192

Collins phantom
kappa(GM)kappa(WM)
0.8770±0.0471 0.9000±0.0465
0.9172±0.0231 0.9392±0.0194
0.8918±0.0618 0.9307±0.0469
0.9014±0.0383 0.9362±0.0323

IBSR2
kappa(GM)
0.7492±0.1321
0.7870±0.0235
0.7822±0.0538
0.8328±0.0271

kappa(WM)
0.8095±0.0985
0.8571±0.0213
0.8655±0.0259
0.8330±0.0366

datasetCollins phantom

IBSR2

kappa(CSF)
kappa(GM)kappa(WM)b) Young2
Preliminary resultskappa(GM)on realkappa(WM)
imagesindicatethat
the intensity
SPM8
0.7683±0.0326 0.8770±0.0471 0.9000±0.0465 0.0750±0.0422 0.7492±0.1321 0.8095±0.0985
VBM8
0.7487±0.0213
0.9172±0.0231the0.9392±0.0194
0.1283±0.0446 quality
0.7870±0.0235
0.8571±0.0213
scaling is0.7281±0.0507
able to improvesegmentation
withlowerFSL
0.8918±0.0618 0.9307±0.0469 0.1244±0.0461 0.7822±0.0538 0.8655±0.0259
our method
0.7491±0.0192 0.9014±0.0383 0.9362±0.0323 0.1387±0.0459 0.8328±0.0271 0.8330±0.0366
underestimation of the GM in the motor cortex and a better
Table 1: Results of the segmentation of the Collins phantom1 with 0, 1, 3, 5, 7, 9% noise and 0, 20, 40, 80 % inhomogeneity,and real datasets of thedatabase (http://www.cma.mgh.harvard.edu/ibsr/).
representationofIBSR2
subcortical
GM (Figure 2).
a)Collins (averageT1)1methodkappa(CSF)a) Collins (average T1)1c)c) Aduld highres3d) Old4c) Aduld highres3d) Old4

Figure4: Shown
the original
biasimagecorrected
the intensity
image(b) and
Figure4: Shown
are theare
original
bias corrected(a), the image
intensity(a),corrected
image (b) corrected
and the inverted
contrast
map(c)
describescontrast
the contrast
betweenthedescribes
estimated tissueclasses. Higher
segmentation
errors aretissue
expect classes.
in regions
thethatinverted
map(c) thatthe contrastbetween
the estimatedwith low contrast (red) and results have to handled more carefully than for regions with good contrast (blue).
Higher segmentation errors are expect in regions with low contrast (red) and results have tohandled more carefully than for regions with good contrast (blue).

Figure 2: Results of our local adaptive segmentation for 4 persons (a-d). In each subfigure the top left images shows the original
bias corrected image and its segmentation below, whereas the right side shows the intensity scaled version of the original on
the top and its segmentation below. For all segment images (bottom of each subfigure) and the intensity scaled image (top left)
the colors are coded in the following way: dark red is WM, yellow is GM, cyan is CSF, and dark blue is background.
1) http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach2) http://fcon_1000.projects.nitrc.org/indi/adhd200/3) http://www.spl.harvard.edu/publications/item/view/2037
4) http://www.oasis-brains.org

Figure 2: Results of our local adaptive segmentation for 4 persons (a-d). In each subfigure the top left images shows the original
Figure2: Results
local adaptive
segmentation
forshows
4 persons(a-d).
In version
each subﬁ
bias
corrected
image andofitsour
segmentation
below, whereas
the right side
the intensity
scaledof the gureoriginaltheonthe
its segmentation
For all segment(bottomof each andsubfigure)and the intensity scaled
image(top left)
toptopleftandimages
showsbelow.
the original
biasimages
corrected
image
its segmentation
below,
whereasthe colors are coded in the following way: dark red is WM, yellow is GM, cyan is CSF, and dark blue is background.

the right side shows the intensity scaled version of the original on the top and its segmentation
1) http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach3) http://www.spl.harvard.edu/publications/item/view/2037
below.
For all segment images (bottom of each subﬁgure) and the intensity scaled image (top2) http://fcon_1000.projects.nitrc.org/indi/adhd200/4) http://www.oasis-brains.org
left) the colors are coded in the following way: dark red is WM, yellow is GM, cyan is CSF, anddark blue is background.

Figure 3 shows the impact of the neighborhood and filter size.
A small neighborhood and low smoothing lead to harder results,
whereas higher values lead to smoother images with less local
differences.
4 neighborhood (9x9x9 box)f) filtering

We have presented a new method that allows a local adaptive
segmentation for varying tissue contrast and a way to appraise
the quality of the segmentation based on the contrast between
the tissue classes.
Another similar idea of local adaptive segmentation is presented
on poster 672 (Gaser et. al. 2012). Compared to the here
described method, it use ROIs to locally equalize the histogram
before segmentation.
Although both methods show visual improvements and good
preliminary quantitative results for the Collins Phantom andIBSR database, further evaluation is necessary for a detailed
understanding of the local changes of the PVE for the GM/CSF
and GM/WM boundaries and depending measures like corticalthickness.

no smoothing

Robert Dahnke, Gabriel Ziegler, and Christian Gaser are supportedby the German BMBF grants 01EV0709 & 01GW0740.

Data3 mm Gaussian smoothing1) http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach2) http://fcon_1000.projects.nitrc.org/indi/adhd200/3) http://www.spl.harvard.edu/publications/item/view/2037
4) http://www.oasis-brains.org5) http://www.cma.mgh.harvard.edu/ibsr/

REFERENCES

Figure 1:1:Main
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Figure 3:
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the size
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segments. Higher neighborhoods and higher smoothing (bottom) reduces local sensitivity a littlebit, but helps to increase the stability.

E-mail: robert.dahnke@uni-jena.deCONCLUSIONSACKNOWLEDGEMENTS

6 mm Gaussian smoothingg) TSEGb)b) Young2d) value estimation of in-class voxels in the neighborhoode) completing of non-class voxels via Laplace-smoothinga)TableResults
ofsegmentation
the segmentationof theCollinswith
phantom¹
1, 3,5,0,7,20,9%
and 0,Table 1:1:
Resultsof theof the Collins
phantom1
0, 1, 3, 5,with
7, 9%0,noise
and40, noise
80 % inhomogeneity,
datasets of the IBSR2
database(http://www.cma.mgh.harvard.edu/ibsr/).
20,
40,and80 real% inhomogeneity,
andreal datasetsof the IBSR2 database.

1 neighborhood (3x3x3 box)c) region growing

kappa(CSF)
0.0750±0.0422
0.1283±0.0446
0.1244±0.0461
0.1387±0.0459

Furthermore, our method allows the description of local tissue
contrast (Figure 4) that may be able to identify special regions or toquantify local segmentation quality. Higher segmentation errors are
expected in regions with low contrast (red) and results have to behandled more carefully than for regions with good contrast (blue).

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PDF of the poster is available at: http://dbm.neuro.uni-jena.de/HBM2012/Dahnke02.pdf