Image enhancement and segmentation of structures in 3D ultrasound images for volume measurements
View Patent ↗A segmentation algorithm is optimized to robustly locate and measure the volume of fluid filled or non-fluid filled structures or organs from imaging systems derived from ultrasound, computer assisted tomography, magnetic resonance, and position emission tomography. A clinical specimen is measured with a plurality of 2D scan planes processed by the segmentation algorithm to estimate the 2D based area the 3D based volumes of fluid-filled and non-fluid filled organs or structures.
1. A method for determining areas of structures from an image, the method comprising:
acquiring a digital image;
enhancing the digital image using non-linear filters;
segmenting the digital image using a spatial gradient algorithm, a initial point positioning algorithm, and an optimal path algorithm that combines the spatial gradient and the initial point positioning algorithms to produce homogeneous regions; and
determining the areas of the homogenous regions.
2. The method of claim 1 , wherein the non-linear filters include a heat filter and a shock filter, the heat filter applied to the digital image followed by application of the shock filter to the digital image.
3. The method of claim 2 , wherein the heat filter is defined by a partial differential equation of an inputted image pixel intensity u expressed in an equation defined as
∂
u
∂
t
=
∂
2
u
∂
x
2
+
∂
2
u
∂
y
2
,
where u is the image being processed,
∂
2
u
∂
x
2
is the second partial derivative of u along the x-axis, and
∂
2
u
∂
y
2
is the second partial derivative of u along the y-axis.
4. The method of claim 2 , wherein the shock filter is a partial differential equation of an imputed image pixel intensity u expressed in an equation defined as
∂
u
∂
t
=
-
sign
(
∂
2
u
d
x
2
)
∂
u
d
x
,
where u is the image being processed,
∂
2
u
∂
x
2
is the second partial derivative of u,
∂
u
d
x
is the absolute value of the first derivative of u along the x-axis.
5. The method of claim 1 , wherein the optimum path algorithm includes a total cost function C(p) weighted between an edge distance cost function, a path direction cost function, and a previous contour distance cost function from the equation
C ( p )=α C e ( p )+β C d ( p )+γ C c ( p ),
where C e (p) is the edge distance cost function, C d (p) is the path direction cost function, C c (p) is the previous contour distance cost function, and α, β, and γ are numerical values.
6. The method of claim 5 , wherein the edge distance function C e (p) is defined from the equation
C
e
(
p
)
=
∑
i
=
p1
p2
(
1
μ
+
∇
I
i
)
,
where I i is the pixel intensity, ∥∇I i ∥ is the gradient magnitude the pixel at location i, and μ is a constant value.
7. The method of claim 5 , wherein the path direction cost function C d (p) is defined from the equation
C
d
(
p
)
=
∑
i
=
p1
p2
(
I
i
i
n
-
I
i
out
)
,
where I i in is the image intensity of the inside and dark pixel and I i out is the image intensities the outside and bright pixel along a path bisecting the I i in and I i out pixels and connecting pixels points i and i −1 adjacent to each side of the I i in pixel.
8. The method of claim 5 , wherein the previous contour function C c (p) is defined by the equation
C
c
(
p
)
=
∑
i
=
p1
p2
D
i
P
,
where P is the previous contour and D i P is the distance of point i from the closest point on the previous contour.
9. A method for determining volumes of structures from a set of images, the method comprising:
acquiring at least two digital images;
enhancing each digital image using non-linear filters;
segmenting the digital image using a spatial gradient algorithm, a initial point positioning algorithm, and an optimal path algorithm that combines the spatial gradient and the initial point positioning algorithmto produce homogeneous regions;
assembling the digital images into an array;
determining the areas of and the volumes between the homogeneous regions in the array.
10. The method of claim 9 , wherein the array includes a rotational assembly, a wedge assembly, and a translational assembly.
11. The method of claim 9 , wherein the non-linear filters include a heat filter and a shock filter, the heat filter applied to the digital image followed by application of the shock filter to the digital image.
12. The method of claim 11 , wherein the heat filter is defined by a partial differential equation of an inputted image pixel intensity u expressed in an equation defined as:
∂
u
∂
t
=
∂
2
u
∂
x
2
+
∂
2
u
∂
y
2
,
where u is the image being processed,
∂
2
u
∂
x
2
is the second partial derivative of u along the x-axis, and
∂
2
u
∂
y
2
is the second partial derivative of u along the y-axis.
13. The method of claim 11 , wherein the shock filter is a partial differential equation of an imputed image pixel intensity u expressed in an equation defined as
∂
u
∂
t
=
-
sign
(
∂
2
u
dx
2
)
∂
u
dx
,
where u is the image being processed,
∂
2
u
∂
x
2
is the second partial derivative of u,
∂
u
dx
is the absolute value of the first derivative of u along the x-axis.
14. The method of claim 9 , wherein the optimum path algorithm includes a total cost function C(p) weighted between an edge distance cost function, a path direction cost function, and a previous contour distance cost function from the equation
C ( p )=α C e ( p )+β C d ( p )+γ C c ( p ),
where C e (p) is the edge distance cost function, C d (p) is the path direction cost function, C c (p) is the previous contour distance cost function, and α, β, and γ are numerical values.
15. The method of claim 14 , wherein the edge distance function C e (p) is defined from the equation
C
e
(
p
)
=
∑
i
=
p1
p2
(
1
μ
+
∇
I
i
)
,
where I i is the pixel intensity, ∥∇I i ∥ is the gradient magnitude the pixel at location i, and μ is a constant value.
16. The method of claim 14 , wherein the path direction cost function C d (p) is defined from the equation
C
d
(
p
)
=
∑
i
=
p1
p2
(
I
i
in
-
I
i
out
)
,
where I i in is the image intensity of the inside and dark pixel and I i out is the image intensities the outside and bright pixel along a path bisecting the I i in and I i out pixels and connecting pixels points i and i −1 adjacent to each side of the I i in pixel.
17. The method of claim 14 , wherein the previous contour function C c (p) is defined by the equation
C
c
(
p
)
=
∑
i
=
p1
p2
D
i
P
,
where P is the previous contour and D i P is the distance of point i from the closest point on the previous contour.
18. A method to determine volume of a structure in digital images acquired from electromagnetic and non-electromagnetic sources, the method comprising:
positioning a transceiver exterior to a patient such that at least a portion of the structure is within a field of view of the transceiver, the transceiver, configured to send electromagnetic radiation and to receive echoes of the electromagnetic radiation;
sending the radiation from the transceiver to the structure;
receiving echoes of the radiation reflected from the structure to the transceiver;
associating the received echoes to form a plurality of 2D scanplanes so that they form an array;
enhancing the images of the structure in each plane of the array using non-linear filters; and
determining the structure volume spanning between and through each plane in the array.
19. The method of claim 18 , wherein plurality of 2D scanplanes are assembled into a plurality of arrays including a rotational array, a translational array, or a wedge array.
20. The method of claim 18 , wherein the non-linear filters include a heat filter and a shock filter, the heat filter applied to the digital images followed by application of the shock filter to the digital images.
21. The method of claim 20 , wherein the heat filter is defined by a partial differential equation of an inputted image pixel intensity u expressed in an equation defined as
∂
u
∂
t
=
∂
2
u
∂
x
2
+
∂
2
u
∂
y
2
,
where u is the image being processed,
∂
2
u
∂
x
2
is the second partial derivative of u along the x-axis, and
∂
2
u
∂
y
2
is the second partial derivative of u along the y-axis.
22. The method of claim 20 , wherein the shock filter is a partial differential equation of an imputed image pixel intensity u expressed in an equation defined as
∂
u
∂
t
=
-
sign
(
∂
2
u
dx
2
)
∂
u
dx
,
where u is the image being processed,
∂
2
u
∂
x
2
is the second partial derivative of u,
∂
u
dx
is the absolute value of the first derivative of u along the x-axis.
23. The method of claim 18 , wherein the plurality algorithms includes a spatial gradient algorithm, a previous contour algorithm, and. an optimal path algorithm that combines the spatial gradient and the previous contour algorithms to produce the homogeneous regions.
24. The method of claim 23 , wherein the optimum path algorithm includes a total cost function C(p) weighted between an edge distance cost function, a path direction cost function, and a previous contour distance cost function from the equation
C ( p )=α C e ( p )+β C d ( p )+γ C c ( p ),
where C e (p) is the edge distance cost function, C d (p) is the path direction cost function, C c (p) is the previous contour distance cost function, and α, β, and γ are numerical values.
25. The method of claim 24 , wherein the edge distance function C e (p) is defined from the equation
C
e
(
p
)
=
∑
i
=
p1
p2
(
1
μ
+
∇
I
i
)
,
where I i is the pixel intensity, ∥∇I i ∥ is the gradient magnitude the pixel at location i, and μ isconstant value.
26. The method of claim 24 , wherein the path direction cost function C d (p) is defined from the equation
C
d
(
p
)
=
∑
i
=
p1
p2
(
I
i
in
-
I
i
out
)
,
where I i in is the image intensity of the inside and dark pixel and I i out is the image intensities the outside and bright pixel along a path bisecting the I i in and I i out pixels and connecting pixels points i and i −1 adjacent to each side of the I i in pixel.
27. The method of claim 24 wherein the previous contour function C c (p) is defined by the equation.
C
c
(
p
)
=
∑
i
=
p1
p2
D
i
P
,
where P is the previous contour and D i P is the distance of point i from the closest point on the previous contour.