Biomedical Engineering Reference
In-Depth Information
as different confidence levels
α
=0
.
05 and
α
=0
.
01, and the null hypothesis
that
µ
o
1
>µ
o
2
was accepted in all previous combinations. The third test was
performed with similar combinations. The null hypothesis was accepted when
d
1
=5and
α
=0
.
01, but it was rejected in all the remaining combinations.
5.
CONCLUSION
This chapter has presented a novel method for investigating brain develop-
mental disorders through minicolumnar structures. A hypothesis-driven work has
been introduced by correlating pathological findings, represented by the number
and width of minicolumns, to MRI findings, represented by volumetric measure-
ment of the total white matter and the outer compartment of the white matter. A
novel level set-based segmentation technique was used to segment the MR images
for both dyslexic and control cases. Signed distance maps and front propagation
have been utilized to parcel the white matter into inner and outer compartments.
The pacellated images were then used to compute and compare the white matter
volumes. Statistical hypotheses tests were performed to investigate the differences
between the volumetric measurements in dyslexic patients and normal control
cases. We concluded that the reduced number of minicolumns in the dyslexic
brains directly affects the volume of the white matter. This work is considered a
beginning of a series of studies of the effect of minicolumnar disturbance on the
brain via MRI. We propose to investigate the relationship between pathological
findings and the geometrical structures of the brain. This will be accomplished by
analyzing the gyral window and bending in the gyrification of normal and dyslexic
cases. Future work will also involve the use of diffusion tensor images to analyze
white matter tracts and correlate it to the way in which minicolumns communicate.
And, last but not least, texture analysis as well as scale representations will be used
to extract more features that may help analyzing brain developmental disorders.
6.
APPENDIX: PSEUDOCODEFORTHESEGMENTATIONALGORITHM
R
+
and
K
regions with initial
means
µ
1
, ..., µ
K
. Assign a level set function for each region
φ
1
, ..., φ
K
. The
main steps will be as follows:
R
n
Assume that we have an image
I
:
→
1. Initialize the level set
function using automatic seed initialization
(
InitF unc
).
2. Calculate mean (
µ
i
), variance (
σ
i
), and prior probability (
π
i
) of each
adaptive region (
P aramF unc
).
3. Mark the narrow band points (
NarrowBandF unc
).
