Segmentation of Stochastic Images using ... - Jacobs University
Segmentation of Stochastic Images using ... - Jacobs University
Segmentation of Stochastic Images using ... - Jacobs University
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Chapter 2 Image <strong>Segmentation</strong> and Limitations<br />
Figure 2.8: <strong>Segmentation</strong> <strong>using</strong> geodesic active contours. Left: The initial image. Right: Solution <strong>of</strong><br />
the geodesic active contour method initialized with small circles inside the object.<br />
Chan and Vese [31] proposed a method that is independent <strong>of</strong> gradient information. Instead, they<br />
proposed to segment homogeneous regions inside the image. To be more precise, Chan and Vese [31]<br />
proposed to minimize the functional<br />
∫<br />
∫<br />
F(c 1 ,c 2 ,C) = µ · Length(C) + ν · Area(inside(C)) + λ 1 |u 0 − c 1 | 2 dx + λ 2 |u 0 − c 2 | 2 dx .<br />
inside(C)<br />
The corresponding Euler-Lagrange equation is<br />
( ( ) )<br />
∇φ<br />
φ t = δ(φ) µ∇ · − ν − λ 1 (u 0 − c 1 ) 2 + λ 2 (u 0 − c 2 ) 2<br />
|∇φ|<br />
outside(C)<br />
(2.39)<br />
, (2.40)<br />
where δ is the Dirac δ-function [42]. This equation is a parabolic PDE that contains a curvature<br />
smoothing term, a term penalizing the segmented area, and two terms penalizing variations from<br />
the mean value <strong>of</strong> the segmented object and the background. Instead <strong>of</strong> δ, we use a regularized<br />
δ-function δ ε for the discretization given by the derivative <strong>of</strong> the Heaviside approximation<br />
H ε = 1 (<br />
1 + 2 ( z<br />
) )<br />
2 π arctan . (2.41)<br />
ε<br />
By <strong>using</strong> H ε from above, δ ε is<br />
1<br />
δ ε (x) =<br />
πε + π . (2.42)<br />
ε<br />
x2<br />
The mean value <strong>of</strong> the object and the background can be computed <strong>using</strong> the Heaviside function:<br />
∫<br />
D<br />
c 1 (φ) =<br />
u ∫<br />
0(x)H ε (φ(x))dx<br />
∫<br />
D<br />
D H , resp. c 2 (φ) =<br />
u 0(x)(1 − H ε (φ(x)))dx<br />
∫<br />
ε(φ(x))dx<br />
D (1 − H . (2.43)<br />
ε(φ(x)))dx<br />
The user chooses λ 1 ,λ 2 , µ,ν. The advantage <strong>of</strong> the Chan-Vese model is that it does not need edges<br />
in the image to segment objects. In fact, the model is independent <strong>of</strong> gradient information. Instead,<br />
it tries to separate homogeneous regions in the image. Fig. 2.9 shows a typical result <strong>of</strong> Chan-Vese<br />
segmentation on an image without edges.<br />
This concludes the presentation <strong>of</strong> classical segmentation algorithms based on PDEs. The presented<br />
segmentation algorithms range from interactive, nearly parameter free algorithms, like random<br />
walker segmentation, over semi-automatic with a moderate number <strong>of</strong> variables, like the level<br />
set based algorithms, to automatic methods like Mumford-Shah segmentation, where no user interaction<br />
is necessary. All these segmentations are able to produce accurate results on a wide range<br />
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