Automated segmentation of image structures
View Patent ↗Methods and systems for segmenting images, wherein the image pixels are categorized into a plurality of subsets using one or more indexes, then a log-likelihood function of one or more of the indexes is determined, and one or more maps are generated based on the determination of the log-likelihood function of one or more of the indexes.
1. A method for segmenting images comprising one or more structures, comprising the steps of,
providing an image comprising a plurality of pixels;
categorizing said pixels into a plurality of subsets using one or more indexes wherein at least one of said indexes comprises a shape index, wherein said shape index comprises a curvature based feature comprising
θ
(
x
,
y
)
=
tan
-
1
(
λ
1
(
x
,
y
)
λ
2
(
x
,
y
)
)
wherein x and y are image coordinates, λ 1 is an extent of curvature along a lowest curvature direction, λ 2 is an extent of curvature along a highest curvature direction, θ is an azimuth angle, λ 1 (x, y)≦λ 2 (x, y) and
-
3
π
4
<
_
θ
(
x
,
y
)
<
_
π
4
;
determining a log-likelihood function of one or more of said indexes;
generating one or more maps of one or more structures in the image based on said determination of said log-likelihood function of one or more of said indexes; and
displaying one or more of the structures on a display device.
2. A method for segmenting images comprising one or more structures, comprising the steps of,
providing an image comprising a plurality of pixels;
categorizing said pixels into a plurality of subsets using one or more indexes wherein at least one of said indexes comprises a normalized-curvature index;
determining a log-likelihood function of one or more of said indexes;
generating one or more maps of one or more structures in the image based on said determination of said log-likelihood function of one or more of said indexes; and
displaying one or more of the structures on a display device wherein said normalized-curvature index comprises a curvature based feature comprising
ϕ
(
x
,
y
)
=
tan
-
1
(
λ
1
(
x
,
y
)
2
+
λ
2
(
x
,
y
)
2
)
1
/
2
I
(
x
,
y
)
wherein x and y are image coordinates, λ 1 is an extent of curvature along a lowest curvature direction, λ 2 is an extent of curvature along a highest curvature direction, φ is a zenith angle, λ 1 (x, y)≦λ 2 (x, y), I(x, y) is an image intensity, and 0≦φ(x, y)≦π/2.
3. A method for segmenting images comprising one or more structures, comprising the steps of,
providing an image comprising a plurality of pixels;
categorizing said pixels into a plurality of subsets using one or more indexes, wherein said pixels are categorized using at least three of said indexes;
determining a log-likelihood function of one or more of said indexes comprising estimating said log-likelihood function of one or more of said indexes and using two out of said three indexes, for an iteration of said step of determining said log-likelihood function, to estimate said log-likelihood of said third index;
generating one or more maps of one or more structures in the image based on said determination of said log-likelihood function of one or more of said indexes; and
displaying one or more of the structures on a display device.
4. The method of claim 3 , wherein said three subsets comprise background, foreground and indeterminate pixels.
5. The method of claim 3 , wherein said log-likelihood is estimated for at least one of said subsets at least in part by estimating one or more decision boundaries.
6. The method of claim 5 , wherein one or more of said decision boundaries are used to apply one or more monotonicity constraints for one or more log-likelihood functions.