Efficient segmentation of piecewise smooth images
AssigneeSiemens Medical Solutions USA, Inc.
InventorsJerome Piovano, Mikael Rousson
Life of this patent
Estimating the term…
Abstract
A fast and robust segmentation model for piecewise smooth images is provided. Local statistics in an energy formulation are provided as a functional. The shape gradient of this new functional gives a contour evolution controlled by local averaging of image intensities inside and outside the contour. Fast computation is realized by expressing terms as the result of convolutions implemented via recursive filters. Results are similar to the general Mumford-Shah model but realized faster without having to solve a Poisson partial differential equation at each iteration. Examples are provided. A system to implement segmentation methods is also provided.
Claims20
A method of segmentation of an image u0 comprising:
- utilizing a processor to create a plurality of smooth regions Ωi delimited by a boundary Γ;
- approximating the image by a piecewise smooth function uσ inside each region Ωi;
- and
- evaluating a functional, wherein the piecewise smooth function is expressed as:
- u
- σ
-
- (
- x
- ,
- Ω
- i
- )
- =
- ∫
- Ω
- i
-
- g
- σ
-
- (
- x
- -
- y
- )
-
- u
- 0
-
- (
- y
- )
-
-
- ⅆ
- y
- ∫
- Ω
- i
-
- g
- σ
-
- (
- x
- -
- y
- )
-
-
- ⅆ
- y
- ,
-
- wherein
-
-
- g
- σ
-
- (
- v
- )
- =
- 1
- 2
-
- π
-
-
- σ
-
- exp
-
- (
- -
- v
- 2
- 2
-
- σ
- 2
- )
- is a Gaussian kernel.
A system of segmentation of an image u0 comprising:
- a processor;
- software operable on the processor to:
- creating a plurality of smooth regions Ωi delimited by a boundary Γ;
- approximating the image by a piecewise smooth function uσ inside each region Ωi;
- and
- evaluating a functional, wherein the piecewise smooth function is expressed as:
- u
- σ
-
- (
- x
- ,
- Ω
- i
- )
- =
- ∫
- Ω
- i
-
- g
- σ
-
- (
- x
- -
- y
- )
-
- u
- 0
-
- (
- y
- )
-
-
- ⅆ
- y
- ∫
- Ω
- i
-
- g
- σ
-
- (
- x
- -
- y
- )
-
-
- ⅆ
- y
- ,
-
- wherein
-
-
- g
- σ
-
- (
- v
- )
- =
- 1
- 2
-
- π
-
-
- σ
-
- exp
-
- (
- -
- v
- 2
- 2
-
- σ
- 2
- )
- is a Gaussian kernel.
Dependencies are inferred from the claim text.