3d segmentation using level set methods. heriot-watt university, edinburgh, scotland zsolt husz...
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3D Segmentation3D Segmentation Using Using
Level Set MethodsLevel Set Methods
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Heriot-Watt University, Edinburgh, Scotland
Zsolt Husz
Mokhled Al-Tarawneh Ízzet Canarslan
University of Newcastle upon Tyne, England
Istanbul Technical University, Turkey
Péter Horváth Sebahattin TopalUniversity of Szeged,
Hungary Middle East Technical University,
Ankara, Turkey
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Input: Medical and/or other images
Operation: Compute gradient image. Define a transform,
for example polar, a cost function, for example
circumference and gradient. Minimize path in
transformed data by cost minimization. Alternative, use
a snake for example using Greedy algorithm. The
object is to find an algorithm to link the points
identified on a gradient map to give continuous
enclosing contours. Think out extension to 3d
Output: Contour (with image)
3D Segmentation Using 3D Segmentation Using Level Set MethodsLevel Set Methods
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Initialization
Gradient
Visualisation / Post-processing
Narrow Band
Reinitialisation Level Set
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• Browse between images• Initialize a sphere• Initialize a region in a slice• Replicate or clear region• Starting process• End program
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Active Contours
)()()( ExtInt EEE
Problems:
• Initialization
• Topological changes
• 3D implementation
2
1
0
22
)(
)('')(')(
IE
dsssE
Ext
Int
[Kass, Witkin, Terzopoulos ’88]
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Level-Set methods
Embed the contour to a higher dimension space level set function: .
dppI
dppI
E
dppINE
EALE
Ext
Ext
Ext
22
22
221
21
12
1
2
))((
2
))(()(
)()(
)()()()(
0
[Osher and Sethian ‘88]
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Level set extension to 3DThe contour moves in a 3D space (3)
Energy minimization: Gradient Descent Methodlocal optimization
)()()()( 1 ExtEVSE
t
E
t
V
t
S
t
E Ext
)()()()( 1
It
E 2)(
zzyyxx IIII 2
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Visualisation
• Interface between algorithms: 3D matrix volume
• 3D volume matrix• Conversion to VRML → flexibility Two approaches:
• triangular mesh• marching cubes
Examples:
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Conclusions• Pros
– noise prone– 3D segmentation is natural– isolated components are permitted
• Cons– LS is parameterised– LS slower than 3D snakes– processing resources (CPU, memory)
• Future work– automatic parameter adjustment– multi-scale processing– combined intensity and edge based segmentation
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References[1] S. Osher and J. A. Sethian, “Fronts propagating with curvature
dependent speed: Algorithms based on Hamilton-Jacobi formulations”, J. Comp. Phys., vol. 79, pp.12–49, 1988
[2] M. Kass, A. Witkin, and D. Terzopoulos. Snakes, “Active Contour Models”, International Journal of Computer Vision 1(4), pp.321–331, 1988
[3] T. Chan and L. Vese, “An Active Contour Model without Edges” in SCALE-SPACE ’99: Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision, pp. 141–151, Springer-Verlag, 1999
[4] C. Xu and J. L. Prince, “Snakes, Shapes, Gradient Vector Flow”, IEEE Transactions on Image Processing, Vol. 7, no. 3, pp. 359-369, 1998
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Thank you for your attention
Questions?