s i e m e n s c o r p o r a t e r e s e a r c h 1 1 general purpose image segmentation with random...
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General Purpose Image Segmentation with Random Walks
Leo Grady Department of Imaging and Visualization
Siemens Corporate Research
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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• About 200 full time research staff• 75+ people working on medical imaging• Basic research clinical products• 1/3 mid/long term research - 2/3 applied projects
SCR
Siemens Medical Solutions
Clinical & UniversityPartners
Princeton, USA
Overview of SCR
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Overview of SCR
Clinical Imaging
• Goals of clinical application software
• Measures something that could not be measured practically before
• Makes diagnosis more accurate or treatment more effective
• Enables therapy that was not possible before
• Increases patient control
• Saves time
• Reduces cost
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Overview of SCRCore interests – Segmentation, registration, visualization
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Offline & Online: Intervention• So far: diagnostic radiology: offline problem
• Interventional imaging: online problem
• Continuous imaging, constant human input
• Rich source of new problems
Overview of SCR
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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General Purpose Segmentation
Goal: Input an image and output the desired segmentation
Why?• Image editing, etc. (e.g., Magic Wand)
• Do not want to reinvent segmentation for each new product
Problem: Two users might want different objects from same image
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General Purpose SegmentationRequires user interaction
NCuts, watershed, mean shift
Snakes, level sets,intelligent scissors
Graph cuts, magic wand region growing
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General Purpose SegmentationAtomic methods:
• Semi-automatic
• Atoms must be subsets of true segmentation
Boundary methods:• Easier to incorporate shape prior• Can be used to improve segmentation of another algorithm• Iterative – local minima• Difficult to initialize automatically and in higher dimension• Harder to generalize to point sets, surfaces, nonuniform sampling, etc.
Seeding methods:• Leads naturally to steady-state and graph-based algorithms
• Easy to seed (even automatically) in arbitrary dimensions
• Generalizes easily to other data modalities
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General Purpose Segmentation
Popular seeding algorithmsRegion growing: Grow segment from initial seed until distance/contrast/etc. requirement is met
• Simple• Fast• Leaks through weak boundaries• Killed by noise
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General Purpose Segmentation
Popular seeding algorithmsGraph cuts: Max-flow/min-cut found between seeds
• Fast• Probabilistic interpretation• Requires lots of seeds to avoid “small cut” problem• Metrication artifacts• True minimum only for two objects (i.e., foreground/background)
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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Given labeled voxels, for each voxel ask: What is the probability that a random walker starting from this voxel
first reaches each set of labels?
Random Walker - Concept
Do not despair – Can be computed analytically!
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Green Red Yellow Blue
Partially labeled image Segmented image
Probabilities
Random Walker - Concept
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Random Walker - Concept
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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Random Walker - Properties
Naturally respects weak object boundaries
Solid border Weak border
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Naturally respects weak object boundariesRandom Walker - Properties
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Provably robust to identically distributed noise
No texture or filtering used – Based purely on intensity weighting
Random Walker - Properties
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1. Segmented regions are connected to a seed
2. The probabilities for a blank image (e.g., all black) yield a Voronoi-like segmentation
3. The expected segmentation for an image of pure noise (identical r.v.s) is equal to the Voronoi-like segmentation obtained from a blank image
Random Walker - Properties
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Random walker
Graph cuts
Random Walker - Properties
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Random Walker - Properties
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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How to compute?
Solution to random walk problem equivalent to minimization of the Dirichlet integral
with appropriate boundary conditions.
The solution is given by a harmonic function, i.e., a functionsatisfying
D[u]=12
Z
(gr u(x;y))2
r ¢gr u = 0
Random Walker - Theory
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Discrete or continuous space?Discrete• Finite
• Exact
• Generalizable
Continuous• Euclidean
• Approximate
• Convergence, etc.
4-connected 8-connected
Random Walker - Theory
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• Mean value theorem:
• Maximum/Minimum principle:
Attractive numerical properties of a harmonic function
xi =
Pwi j (xi ¡ xj )P
wi j
min(xBoundary) · xInterior · max(xBoundary)
Random Walker - Theory
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Need to represent Laplacian on a graph:
In the notation of algebraic topology, the Laplacian is given by
0-coboundary operator (since we operate on nodes) is theincidence matrix:
With the constituitive matrix Ceij eij=wij playing the role of the
metric tensor, the combinatorial Laplace-Beltrami operatoris given as
Random Walker - Theory
r 2u = @±
Aei j vk =
8><
>:
+1 if i = k;¡ 1 if j = k;0 otherwise
L = AT CA
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D[u]=12
Z
(gr u(x;y))2
Subject to boundary conditions at seed locations
xF = 1; xB = 0
Energy functional:
r ¢gr u = 0 Lx = 0Euler-Lagrange:
D[x]=12
¡xT AT
¢C (Ax) =
12xT Lx
Random Walker - Theory
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Random Walker - Theory
Laplacian matrix defined by graph as:
Decompose Laplacian matrix into labeled (marked) and unlabeled blocksand define an indicator vector for the marked nodes:
Must solve a sparse, SPD, system of linear equations for probabilities
Since probabilities must sum to unity, for K labels, only K-1 systems must be solved
L vi vj =
8><
>:
dviif i = j ;
¡ wi j if vi andvj areadjacent nodes;0 otherwise
L =
·LM BB T LU
¸ms
j =
(1 if Q(vj ) = s;0 if Q(vj ) 6= s:
LUxs = ¡ Bms
xK = 1¡X
i<K
xi
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Input image Overlaid graph (lattice) Edge strength (line width) encodes image gradient
Random walk formulated on a lattice (graph) that represents the image
Random Walker - Concept
wi j = e¡ ¯ (I i ¡ I j )2
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Therefore, we can formulate a combinatorial Dirichlet integral:
Represents minimum power distribution of an electrical circuit
We can analytically solve the equivalent circuit problem for the random walker probabilities
Random Walker - Theory
D[x]=12
¡xT AT
¢C (Ax) =
12xT Lx
AT z = f (Kirchho®'s Current Law);
Cp = z (Ohm's Law);
p = Ax (Kirchho®'s VoltageLaw);
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Situation exactly analogous to DC circuit steady-state
Labels – Unit voltage sources or groundsWeights – Branch conductancesProbabilities – Steady-state potentials
Label 1 prob.
Initial labeling
Label 2 prob. Label 3 prob.
Random Walker - Theory
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Algorithm summary:
1. Generate weights based on image intensities
2. Build Laplacian matrix
3. Solve system of equations for each label
4. Assign pixel (voxel) to label for which it has the highest probability
Random Walker - Theory
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Equally valid interpretations of algorithm:1. What is the steady-state temperature distribution in the
inhomogeneous domain, given fixed temperatures at the seeds?
2. What is the probability that a random walker leaving this node first reaches a label of each color?
3. What is the electrical potential at this node when the labeled nodes are fixed to unity voltage (w.r.t. ground)?
4. What is the (normalized) effective resistance between this node and the labeled nodes?
Random Walker - Theory
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Equally valid interpretations of algorithm:
5. If a 2-tree (tree with a missing edge) is drawn randomly, what is the probability that this node is connected to each label?
Interpretation used to prove noise robustness
Random Walker - Theory
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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Main computational burden is solving the system of linear equations
Fortunately, system is sparse, symmetric, positive definite For a lattice (or any regular graph), the sparsity structure of the matrix
is circulant
Random Walker - Numerics
LUxs = ¡ Bms
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• Structure of the Laplacian matrix allows for efficient storage and operations – Off diagonals may be packed into RGBA
• Progressive visualization of solution possible
• Z-buffer allows masking out of seeds
Advantages of a GPU implementation
Random Walker - Numerics
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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Random Walker - Results
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Random Walker - Results
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Random Walker - Results
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Random Walker - Results
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Cardiac segmentation across modalities
Random Walker - Results
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Segmentation of objects with varying size, shape and textureRandom Walker - Results
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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Original image Priors only
Random walkeronly
Combined
Possible to incorporate other terms – Intensity priors
Useful for multiple, disconnected objects
Random Walker - New
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Random Walker - New
wi j = e¡ ¯ (I i ¡ I j )2Gaussian weighting
Systematic study of weighting function
Reciprocal weighting
wi j =1
1+¯ (I i ¡ I j )2
Run on 62 CT datasets with seeds and manual segmentations
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Random Walker - New
Systematic study of edge topology
6-connected
10-connected
26-connected
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Random Walker - New
Formulate as special case of general segmentation approach -Compare with other instances of algorithm
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Random Walker - New
5 eigs 20 eigs 40 eigs 60 eigs 80 eigs 100 eigs Exact
Precomputation
• Precompute eigenvectors of Laplacian• Input seeds• Instant result (approximation)
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Outline
• Overview of Siemens Corporate Research (SCR)
• General purpose segmentation
• Random walker algorithm– Concept– Properties– Theory– Numerics– Results– New
• Conclusion
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Conclusion
1. General-purpose2. Robust to noise and weak boundaries3. Has a single parameter (not adjusted for these results)4. Stable5. Accurate6. Available
Random walker algorithm is:
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Conclusion – More Information
My webpage:http://cns.bu.edu/~lgrady
Random walkers paper:http://cns.bu.edu/~lgrady/grady2006random.pdf
Random walker demo page:http://cns.bu.edu/~lgrady/Random_Walker_Image_Segmentation.html
CVPR Short Course: Fundamentals linking discrete and continuous approaches to computer vision - A topological view
http://cns.bu.edu/~lgrady/Short_Course.html
MATLAB toolbox for graph theoretic image processing at:http://eslab.bu.edu/software/graphanalysis/
Writings and code
Random walkers MATLAB code:http://cns.bu.edu/~lgrady/random_walker_matlab_code.zip