Note: Descriptions are shown in the official language in which they were submitted.
CA 02617671 2012-11-21
SYSTEM AND METHOD FOR AUTOMATIC SEGMENTATION OF VESSELS
IN BREAST MR SEQUENCES
Technical Field
This invention is directed to segmentation of digitized medical images.
Discussion of the Related Art
Contrast enhanced MR sequences are a powerful diagnostic tool for the
detection of lesions in breast. Typically, the diagnosis begins by identifying
suspicious
regions of enhancement in post contrast acquisitions with respect to a pre-
contrast one.
Automating this process is therefore one that a computer aided detection
system needs
to perform. A difficulty for such a system is the fact that, besides the
lesions, a number
of non-suspicious structures also enhance in the post-contrast image. Most of
these
structures are vessels. Vessels are the main type of false positive structure
that arise
when automatically detecting lesions as regions that are enhanced after
injection of the
contrast agent.
Dynamic subtraction of post-contrast Ti weighted images is routinely
performed as part of a protocol to evaluate breast lesions with magnetic
resonance
imaging (MRI). Because lesions usually contain a high vascularity, perfusion
of a
contrast agent makes the lesions appear brighter than the background and
therefore this
modality is quite sensitive. Automatically segmenting the lesions can provide
the
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radiologist with accurate automatic measurements and render these measurements
more
consistent across readers. Region growing segmentation algorithms or even
simple
thresholding could be used to segment those lesions, if it was not for the
fact that the
vessels that are attached to them cause the segmentation to leak through the
vessels.
Removing the vessels could therefore facilitate the segmentation task. On the
other
hand, automatic detection of the lesions requires the ability to distinguish
the lesions
from the various types of normal structures that also enhance with the
contrast agent.
These include breast parenchyma, vessels, the area under the nipples and the
area
surrounding the heart. There has been interest in developing automatic methods
for
segmenting the vascular structure in modalities like CT and MR angiography,
etc. The
literature is very abundant on this subject, describing both automatic and
semi-
automatic methods, which cover a very wide range of models, assumptions and
techniques. In a clinical work-flow context, the extraction of the vascular
structure
should be fully automatic and require no more than a few seconds of
computation time.
One technique that performs well, can be easily validated with clinical data,
and is
easily implemented, involves the use of moments, for which there is little
reported in
the research literature. Previous approaches based on moments includes the use
of
moment invariants to extract and characterize vessels in infrared images of
laser-heated
skin, the use of geometrical moments to extract the vascular structure from
large CT
data sets, as well as to characterize the vessels, and computing multi-
resolution moment
filters for the extraction of linear structures from very noisy 2D images.
The use of geometrical moments to extract image structure varies among
methods proposed in the literature. Many times, the moments of inertia are
computed
on a binarized image obtained after thresholding. The problem with this is
that the
threshold is usually difficult to choose and might not allow detection of
small vessels
because a low threshold will cause smaller vessels, which tend to have lower
intensities, to be merged with neighboring structures. Another problem with
thresholding is that the structure becomes "pixelized", i.e. develops sharp
edges that
render the computation of its shape imprecise with respect to the true shape
of the
underlying structure.
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An alternative to thresholding is to compute the moments using the image
intensity function f as density function. However, in regions where the signal-
to-noise
(SN) ratio is low, it becomes difficult to establish a threshold on the
eccentricity of a
fitted ellipse to detect elongated structures. For example, FIG. 1(a) depicts
an MlP of a
sub-volume extracted from a real image around a vessel junction. The top row
depicts
the original voxel values using nearest-neighbor interpolation. The middle row
depicts
the binary image obtained after manual thresholding. The threshold was
adjusted to
capture both vessels, a task that is quite difficult to achieve automatically.
The
pixelization effect of the thresholding is evident, which affects the
precision of the
shape descriptors. The third row shows the same sub-volume using a more
sophisticated interpolation scheme.
Summary of the Invention
Exemplary embodiments of the invention as described herein generally include
methods and systems for automatic detection of bright tubular structures and
its
performance for automatic segmentation of vessels in breast MR sequences based
on
geometrical moments for the extraction of tubular structures from images. A
method
according to an embodiment of the invention is based on the eigenvalues of the
shape
tensor, and reconciles not having to threshold the image with reliably
recovering
structure under very low signal to noise (SN) ratios. A method according to an
embodiment of the invention does not rely on image derivatives of either first
order,
like methods based on the eigenvalues of the mean structure tensor, or second
order,
like methods based on the eigenvalues of the Hessian, and the smoothing of the
output
which is inherent to approaches based on the Hessian or structure tensor is
avoided. A
method according to an embodiment of the invention can execute quickly,
needing only
a few seconds per sequence. Testing results based motion-corrected breast MR
sequences indicate that a method according to an embodiment of the invention
reliably
segments vessels while leaving lesions intact, and out-performs differential
techniques
both in sensitivity and localization precision and is less sensitive to scale
selection
parameters.
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According to an aspect of the invention, there is provided a method for
segmenting digitized images including providing a digitized image comprising a
plurality of intensities corresponding to a domain of points on a 3-
dimensional grid,
defining a shape matrix for a selected point in said image from moments of the
intensities in a window of points about said selected point, calculating
eigenvalues of
said shape matrix, determining an eccentricity of a structure underlying said
point from
said eigenvalues, and segmenting said image based on said eccentricity values,
wherein
the steps of defining a shape matrix, calculating eigenvalues of said shape
matrix, and
determining the eccentricity of the underlying structure are repeated for all
points in
said image.
According to a further aspect of the invention, the selected point has a
median
enhancement greater than a predefined threshold, wherein a contrast enhancing
agent
was applied to the subject matter of said digitized image prior to acquisition
of said
image.
According to a further aspect of the invention, the median enhancement is
calculated by taking a difference of a median value of said contrast enhanced
image and
a median value of a pre-contrast enhanced image, and normalizing said
difference to be
within a predefined range.
According to a further aspect of the invention, the shape matrix Sa is defined
as
( õ
1-`5cc,tx II ma lixz,a
S := 11xy,a yy,a Ityz,a2
vllxz,a Ityz,a zz,a
wherein
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2
m2,0,0 ,a m1,0,0,a
m m2 '
0,0,0,a 0,0,0,a
2
m1,1,0,a m
1,0,0,am 0,1,0,a 10,2,0,a m0,1,0,a
1.1 yy,a
m2 /72 2
0,0,0 m
0,0,0,a 0,0,0,a
2
1711,0,1,a M1,0,0,a/720,0,1,a M0,1,1,a 172
0,1,0,aM 0,0,1,a M0,0,2,a
1710,0,1,a
flyz,a 2 Pzz,a 2
17 2 1/2
/720,0,0,a Z0,0,0,a 0,0,0,a mm000 10,0,0,a
wherein moments mp,q,r,,, are defined as
Yo, zo )=
wherein w is a window function with compact support, p, q, r1u0 and apl.
According to a further aspect of the invention, the integral is calculated by
a
sum over a finite neighborhood about each point.
According to a further aspect of the invention, the window function is defined
by
x E Nxvx,Nxvx]
\ 1 if yEkNyvy,Nyvy]
Z E {¨NzVz,NzVzi
0 otherwise
wherein vx, vy, vz are image point spacings, Nx, Ny, N, are non-negative
integers defined
wherein a window size contains a largest diameter of interest.
According to a further aspect of the invention, the method comprises computing
said moments using nearest neighbor interpolation, and correcting said shape
matrix
according to
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/ 2
Vx 0 0 \
^ 1
IS, +- 0 -v2 0 ,
12 Y
0 0 V2
\ z ,/
wherein vx, vy, vz are image point spacings.
According to a further aspect of the invention, the method comprises computing
said moments using trilinear interpolation.
According to a further aspect of the invention, a=1, and correcting said shape
matrix according to
I' 2
Vx 0 0\
1
0 V2 0 ,
6 Y
0 0 V2
\ z i
wherein vx, vy, vz are image point spacings.
According to another aspect of the invention, there is provided a program
storage device readable by a computer, tangibly embodying a program of
instructions
executable by the computer to perform the method steps for segmenting
digitized
images.
Brief Description of the Drawings
FIG. 1(a) depicts an MIP of a sub-volume extracted from a real image around a
vessel junction, according to an embodiment of the invention.
FIG. 1(b) depicts a simulated vessel and its detection using moments of
inertia
without thresholding, according to an embodiment of the invention.
FIG 2 illustrates basis functions used for 1D linear interpolation, according
to
an embodiment of the invention.
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FIGS. 3(a)-(c) depict segmentation of a large lesion, according to an
embodiment of the invention.
FIGS. 4(a)-(c) depicts segmentation of multiple small lesions, according to an
embodiment of the invention.
FIG 5 depicts segmentation of the vascular structure in breast MRI using the
shape tensor, according to an embodiment of the invention.
FIG. 6 depicts a flow chart of a method for a moment-based segmentation
according to an embodiment of the invention.
FIG. 7 is a block diagram of an exemplary computer system for implementing a
moment-based segmentation method according to an embodiment of the invention.
Detailed Description of the Preferred Embodiments
Exemplary embodiments of the invention as described herein generally include
systems and methods for automatic detection of bright tubular structures and
its
performance for automatic segmentation of vessels in breast MR sequences. A
method
according to an embodiment of the invention is based on the eigenvalues of a
shape
tensor. It can be compared to methods based on the eigenvalues of the mean
Hessian
and those based on the eigenvalues of the mean structure tensor. The Hessian,
being
defined from the second-order derivatives, can be regarded as a structure
descriptor of
order two. Similarly, the structure tensor is a structure descriptor of order
one. The
shape tensor can be regarded as a structure descriptor of order zero.
As used herein, the term "image" refers to multi-dimensional data composed of
discrete image elements (e.g., pixels for 2-D images and voxels for 3-D
images). The
image may be, for example, a medical image of a subject collected by computer
tomography, magnetic resonance imaging, ultrasound, or any other medical
imaging
system known to one of skill in the art. The image may also be provided from
non-
medical contexts, such as, for example, remote sensing systems, electron
microscopy,
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etc. Although an image can be thought of as a function from R3 to R, the
methods of
the inventions are not limited to such images, and can be applied to images of
any
dimension, e.g. a 2-D picture or a 3-D volume. For a 2- or 3-dimensional
image, the
domain of the image is typically a 2- or 3-dimensional rectangular array,
wherein each
pixel or voxel can be addressed with reference to a set of 2 or 3 mutually
orthogonal
axes. The terms "digital" and "digitized" as used herein will refer to images
or
volumes, as appropriate, in a digital or digitized format acquired via a
digital
acquisition system or via conversion from an analog image.
A method according to an embodiment of the invention works on the image
intensities by computing second-order geometric moments of the underlying
(bright)
structure. A method can be applied to a binarized image obtained by applying a
threshold to the initial post-contrast enhanced image, but a method can be
applied
without this threshold. The eigenvalues of the second-order geometric moments
are a
classical tool for shape characterization in object recognition. They,
however, have
never been applied as a filter for extracting image structure. Given a binary
image, a
small sub-volume around each pixel (its size being related to the structures
of interest)
is considered and a shape tensor is defined at that location as the second-
order moments
of the positions of the bright voxels with respect to the center of the sub-
volume. For
voxels in which the center pixel is both bright and lies close enough to the
center of the
underlying shape, eigenvalues of the shape tensor are computed and assigned
the value
2i-22/(?4+22) to the filter response, where 2,2>2,1 are the largest
eigenvalues.
According to an embodiment of the invention, a geometrical 3D moment can be
defined as:
1flp,q,r,a(X013 01Zo) L,(x¨x0)P(y¨y0)q(z¨z0)rf(x,y,z)aw(x¨x0,Y¨ , z -
zo)dxdydz,
where w is a positive and symmetric window function with compact support that
provides localization, p, q, r,u0 and a1ul. The shape tensor of order a is
defined in
terms of these moments as
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( õ
i"xx,a zy,a
S a = 113,3,,a flyz,cr
\Pxz,a yz,a Alma
where
m 2
2,0,0,a 1,0,0,a
=
M2
0,0,0,a
2
M1,1,0,a in1,0,04M0,I,0,a m0,2,0,a m0,1,0,a
Pxy,a2 d".YY,a =--
111 2
1110,0,0,a 0,0,0,a m0,0,0, Ma 0,0,0,a
M1,0,1,a M1,0,0,a M0,0,1,a m0,1,1,a M0,1,0,aM0,0,1,a
1720,0,24 M 2
0,0,1,a
Pxz,a1120,0,0,ce 2 iR'yz,a 2 azz,a
2
M000 M0,0,0,c 0,0,0,a
M0,
0,0,ce
This matrix is symmetric, so all of its eigenvalues are real. Letting the
three
eigenvalues be 23A2>241.10, a filter response can be defined by
Cline = 2
+22
For a line or cylindrical like structure such as a vessel, Cuneill.
According to an embodiment of the invention, the eccentricity of the
underlying
shape is computed based on the eigenvalues ORi[X2R3 of Sa, with a>>1. As a
becomes larger, the higher intensity values are given more importance, acting
almost
like a thresholding. High values of a can cope with very low SN ratios as
shown in the
simulated experiment of FIG. 1(b), were a synthetic tubular structure with
added
uniform noise is detected with the classic matrix of inertia and the shape
tensor at a=15.
FIG. 1(b) depicts a simulated vessel and its detection with the standard
moments of inertia without thresholding and with the shape tensor at a=15. The
columns show from left to right: (1) the center slice of the original
synthetic volume,
(2) its maximum intensity projection (M1P), (3) the MW of the volume with the
vessel
removed by the standard moment method, (4) the MW of the detected vessel by
the
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moment method, (5) the MIP of the volume with the vessel removed using the
shape
tensor with a=15, and (6) the MIP of the detected vessel using shape tensor
with a=15.
The six rows represent increasing levels of additive uniform noise, giving
respectively
SN ratios of, from top to bottom: (1) 56.3, (2) 36.7, (3) 20.4, (4) 11.6, (5)
5.5 and (6)
0.8 dB. The threshold on the eccentricity of the shape is the same across rows
for each
23 23
algorithm. In all cases the detection criterion was ¨ > 15 for S15 and ¨ > 2
for the
22 22
matrix of inertia corresponding to Si. This improved detection performance has
been
noticed in real cases.
In practice, the above integral is usually replaced by a sum over a finite
neighborhood around each voxel since f is only known at voxel locations. It
can be
assumed for all experiments that the localization function is given by
x E {-- Nxvx,Nxvx]
\ 1 if yE [¨NyVy,NyVyi
w(x, y,
z E [¨NzVz,NzVzi
0 otherwise
where vx, vy, I), are the image voxel spacings and AT, Ny, N, are non-negative
integers
defined such that the window size contains the largest diameter of interest.
Then, given
an image, consider a small sub-volume around each pixel and define
2Nx 2Ny 2Nz
= IEI(ivx)Ptivy)q (1CV Jr Pt k
p,q,r,a
1=1 j=0 k=0
where piik is the value of the image at the voxel corresponding to the indexes
i,j,k. The
eigenvalues 0N22I23 of the matrix
r
Pxy,ce icz,ce\
a =Iy,a flyy,a 1 yz,a
ftxz,a fl yz,a
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are computed where the values ft.. are computed as above but using the
summation
moments. The eccentricity or elongation of the underlying structure can be
measured
by the classic eccentricity measure 6=(.1,3-.12)/(.13+,12), which takes values
between 0 and
1, or simply by the ratio X3/X2, provided that k2>0.
Since moment based methods do not assume differentiability of the image
intensity function f, simple interpolation schemes can be used such as nearest-
neighbor
or tri-linear, to compute integrals of the interpolated function instead of
sums over the
voxel values. One may expect better precision using the value of these
integrals,
especially in the case of tri-linear interpolation. Using the equalities
i+1/2),õ
, x dx ¨ ¨ v3
fi-1 1 2)v x -
i(i-1-112),,x
xdx = v x2 i,
4i-1/2)v
J.-I-112)v, 2
li3 ( / = 2 -F 1
X dX = ¨ ,
I
/ 2)vx ,c 12,,
it can be seen that, for the nearest-neighbor interpolation integral, the
matrix 8a above
should be replaced by
\
iv 2 0 0
^ 1 x
Sa -I- ¨ 0 V2 ), 0 .
12
0 0
\ z v21
In the case of tri-linear interpolation, the function f is given by I piik guk
, where
ij,k are the indices of the image vexels, piik is the image value at a voxel
and
I
gi,j,k (x3Y,z)= :1 Ix¨ vxir
i\1 vx )
0 otherwise 1Y¨vYj(
n1 I
V Y j \ Z v k
¨ z I
v
z J x E Ki ¨ 1)17x ,0 + 1)v x]
if
ZE Kk ¨1)liz,(k +1)Vz I
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k+i)vz41-0yx /0+1)vxOdxdydz
Then, writing SO = :
k-oyz i-oyy
x.yz
Igo = VxV yVõ
xyz
= 2
Xg =
ijk /Vx VyVõ
Lz =
r .Y6= uvx2 v y2 v z
= 2 1 3
.Lx 2 g iik =¨ V xV V ,
6 Y z
so that, for tri-linear interpolation in the case a=1, the matrix ga should be
replaced by
2
Vx 0
^ 1
+ ¨6 0 V2 0 .
0 0 z2
The situation becomes more complex in the case of the general shape tensor
(a>1)
using tri-linear interpolation, in which f is given by tj py..ky g..k =
Although the
k
corresponding integrals are still computable in closed form, the complexity is
increased
significantly. According to an embodiment of the invention, to compute the
corresponding shape tensor, note that it is no longer useful to compute
moments of grik,
as in the case a=1 above. To proceed, the above moments can be obtained using
a less
direct method but which can be generalized to a>1. This can be done in the 1-D
case,
with the 2-D and 3-D cases being straightforward generalizations thereof.
Assuming
pk=0 for k<i-2 or k>i+2, one obtains
i(i+2)yx i+1 r+l)vx
4i-2)vxf (X)dX = k-1)vx Pk gk (x)
dx
k=i-1
fi--1)vx vx
= p. (x)dx + g . (x)+ pig i(xpdx
i-2.)vx g i.-ovx
+ 4 (p ig i(x)+ p g i+,(4)dx + fi+2)vx p,lg
i+1(x)dx
vx i+l)vx
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= vx + Pi + P i+i) =
The four integrals above can be obtained from the 3 piecewise-linear basis
functions illustrated in FIG. 2. Referring to the figure, the first basis
function gjj is
defined over the domain (i-2)vx to iv, the second basis function gi is defined
over the
domain (i-1)vx to (i+/)vx, and the third function gt+i is defined over the
domain iv x to
(i+2)vx.
This method of computing the integral can be generalized to a>1. For instance,
one can compute:
õ2 ((i-1)v2 vx /
xf cbc .1i-2)vx f \" dx+ t-
ovxkp 1,g i(x)+ pig ikx )) ax
rovx)v 2
x
+ x (pig i(x)+ pi+ig i+,(x))2 i+2dx + t
(pi+ g (4 dx
i+ovx
(2 2 1 2 1 2 2
= \-3 Pi-1 +-3 +¨p312 -I--
3PiPi-g+-3-131+1 ivx
Similarly,
(
i+2)vx r \ r
1 1 1 1 \
xf cbc iPi2 + (i +2i +2i + ¨4 jPi + (i + 1)
1'2+1 vx2
and
(11 4. 2.2\ 2 (1 1. 1.2\
+--3-/+--it ! P+
r_2)õx x2f (x)2 dx 3
V,
4i-2)v r 1 2 .2N 2 1 1. 1 .2 \ (11 4 2.2\
2
- ¨t pi + ---1+¨t + +-1
3 10 3 3 05 3 3 ,
Although generalized formulas could potentially be found for the 3-D case and
a given
a> 1, the complexity of the resulting polynomials is quite high for the
potential
precision improvement. In the 2-D case, the four integrals above become
sixteen
integrals, and become sixty-four integrals in 3D.
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A method according to an embodiment of the invention has been tested on more
than 100 motion-corrected breast MR dynamic sequences. The results obtained
show
that vessels can be reliably segmented while leaving lesions intact. According
to an
embodiment of the invention, moments are computed on a sliding window of fixed
size, but only points for which the median enhancement is higher than a given
threshold
are considered. This threshold can be chosen low enough so as to detect even
small
vessels. It is not difficult to set because it is not relied upon for the
computations but
only to accelerate the whole process, by treating fewer voxels. The median
enhancement is calculated by taking the median value of the post-contrast
acquisitions
minus the value of the pre-contrast acquisition at each image voxel. This
difference is
then normalized by applying an affine function such that the resulting
enhancement is
in the range [0, 200]. FIGS. 3(a)-(c), 4(a)-(c), and 5 show a few
representative
examples of the results.
FIGS. 3(a)-(c) depict segmentation of a large lesion, while FIGS. 4(a)-(c)
depicts segmentation of multiple small lesions. For both of these figures,
panel (a)
depicts a thresholded initial post-contrast enhancement image, panel (b)
depicts the
detected vessels, and panel (c) depicts the lesions with the vessels removed.
FIG 5 illustrates segmentation of the vascular structure in breast MRI using
the
shape tensor with a=6. The three columns show orthogonal views of the same
patient.
The first row shows the original MT of the median enhancement. The second row
shows the same volume with automatically removed vessels. The third row shows
a
MW of the removed vessels alone. Notice that vessels of very different
diameters and
enhancement levels are correctly segmented. The detection was performed by
taking
locations for which the eigenvalues of the shape tensor were such that /13 /22
> 3.
In each of these figures, note how even small vessels are correctly segmented
and even small spherical structures are left intact. As a further validation,
a method
according to an embodiment of the invention extracted the vascular structure
on 40
cases reviewed by three radiologists who marked a total of 75 lesions. The
vessels
were correctly segmented in all the cases and all the marked lesions were left
intact.
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A flow chart of a moment-based segmentation method according to an
embodiment of the invention is depicted in FIG. 6. Referring now to the
figure, an
image to be segmented is provided at step 61. The shape tensor is calculated
for voxel
in the image whose median contrast enhancement exceeds a pre-defined
threshold, as
determined at step 62. The moments from which the shape tensor is defined are
calculated at step 63 on a fixed size window about the selected voxel. At step
64, the
eigenvalues of the shape tensor are calculated, and at step 65, the
eccentricity of the
underlying structure is determined. The process loops at step 66 until every
voxel has
been processed. The image is segmented at step 67 based on the eccentricities
derived
from the shape tensor.
Moment-based methods to extract local shape information can be compared to
methods based on higher order image derivatives. For instance, the Gradient
Square
Tensor (GST), or structure tensor, has been proposed as a robust method to
estimate
local structure dimensionality. It is based on first order derivatives and
hence could be
called a structure descriptor of order one. The eigenvalues of the Hessian
also provide
local image structure information, as well as the principal curvatures of the
isolevel at a
given point. The Hessian and the principal curvatures are defined from second-
order
derivatives and hence could be called structure descriptors of order two. The
shape
tensor can be seen as a structure descriptor of order zero. It is based on
integrals and
hence has the property of being very robust to noise compared to methods based
on
either first or second order derivatives. In addition, there is no need to
assume any
differentiability on the image function, which simplifies the modeling. A
problem with
a shape tensor based method is that junctions are not detected. Also, a better
understanding is needed to determine whether geometrical shape properties
could be
computed from the eigenvalues of the shape tensor with a>1.
It is to be understood that the present invention can be implemented in
various
forms of hardware, software, firmware, special purpose processes, or a
combination
thereof. In one embodiment, the present invention can be implemented in
software as
an application program tangible embodied on a computer readable program
storage
device. The application program can be uploaded to, and executed by, a machine
comprising any suitable architecture.
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FIG. 7 is a block diagram of an exemplary computer system for implementing a
moment-based segmentation method according to an embodiment of the invention.
Referring now to FIG. 7, a computer system 71 for implementing the present
invention
can comprise, inter alia, a central processing unit (CPU) 72, a memory 73 and
an
input/output (I/0) interface 74. The computer system 71 is generally coupled
through
the I/O interface 74 to a display 75 and various input devices 76 such as a
mouse and a
keyboard. The support circuits can include circuits such as cache, power
supplies,
clock circuits, and a communication bus. The memory 73 can include random
access
memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a
combinations thereof. The present invention can be implemented as a routine 77
that is
stored in memory 73 and executed by the CPU 72 to process the signal from the
signal
source 78. As such, the computer system 71 is a general purpose computer
system that
becomes a specific purpose computer system when executing the routine 77 of
the
present invention.
The computer system 71 also includes an operating system and micro
instruction code. The various processes and functions described herein can
either be
part of the micro instruction code or part of the application program (or
combination
thereof) which is executed via the operating system. In addition, various
other
peripheral devices can be connected to the computer platform such as an
additional data
storage device and a printing device.
It is to be further understood that, because some of the constituent system
components and method steps depicted in the accompanying figures can be
implemented in software, the actual connections between the systems components
(or
the process steps) may differ depending upon the manner in which the present
invention is programmed. Given the teachings of the present invention provided
herein, one of ordinary skill in the related art will be able to contemplate
these and
similar implementations or configurations of the present invention.
While the present invention has been described in detail with reference to a
preferred embodiment, those skilled in the art will appreciate that various
modifications
16
CA 02617671 2012-11-21
and substitutions can be made thereto. The scope of the claims should not be
limited
by the embodiments set out herein but should be given the broadest
interpretation
consistent with the description as a whole.
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