heat kernel smoothing, hot spots conjecture and fiedler...
TRANSCRIPT
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Heat kernel smoothing, �hot spots conjecture �and Fiedler vector
MooK.Chung
UniversityofWisconsin-Madison
www.stat.wisc.edu/~mchung
2017 September 5, Casa Mexicana Oaxaca
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Abstracts The second eigenfunction of the Laplace-Beltrami operator (often called Fiedler vector in discrete settings) follows the pattern of the overall shape of an object. This geometric property is well known and used for various applications including mesh processing, feature extraction, manifold learning, spectral embedding and the minimum linear arrangement problem. Surprisingly, this geometric property has not been precisely formulated yet. This problem is directly related to the somewhat obscure hot spots conjecture in differential geometry that postulates the behavior of heat diffusion near boundary. The aim of the talk is to discuss and raise the awareness of the problem. As an application of the hot spots conjecture, we show how the second eigenfunction alone can be used for shape modeling of elongated anatomical structures such as hippocampus and mandible, and determining the diameter of large-scale brain networks. This talk is based on Chung et al. 2015 Medical Image Analysis 22:63-76.
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Acknowledgements
Yuan Wang, Houri Voperian, Andrew Alexander, Seth Pollack,
Richard DavidsonUniversity of Wisconsin-Madison
SeongHoSeoSeoul National University
NIH funding: EB022856, MH098098, MH061285
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Diffusion smoothing
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Diffusion smoothingwith boundary condition
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2001, Proceedings of the 53rd Session of the International Statistical Institute
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FEM-based discretization of LB-operator
Proved in Lopez-Perez et al, 2004, ECCV
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3T MRI tissue
segmentation
surface extraction
triangle mesh with 1 million
triangles
Yellow: outer cortical surface
Blue: inner cortical surface
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5mm FWHM Diffusion Smoothing
0.00
0.01
mean curvature 20 iterations 100 iterations
Matlab code: http://brainimaging.waisman.wisc.edu/~chung/diffusion
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Heat kernel smoothing
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Smoothing in manifold
Anatomical manifold
parameter spacemanifold
Hilbert space with inner product
Self-adjoint operator
Parameter space
Basis function
L2(M)
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Analytic approach to heat kernel smoothing
t = scale, bandwidth, diffusion time
Analytic representation
Input signal
Kernel smoothing
Weighed Fourier series
PDE:
Chung et al., 2007. IEEE Transactions on Medical Imaging 26:566-581
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X-coordinate smoothed with bandwidth 10and 1269 eigenfunctions
Heat kernel smoothing on manifold
K� ⇤ X(p) =1X
j=0
e��j�Xj j(p)
Xj = hX, ji
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Discretize basis functions via FEM
�f = �f C⇥ = �A⇥
A C
MATLAB code:http://brainimaging.waisman.wisc.edu/~chung/lb
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K� ⇤ X(p) =1X
j=0
e��j�Xj j(p)
X0 X1 X2 X3
Functional approach to heat kernel smoothing
Seo et al. 2010 MICCAI 6363:505-512Chung et al. 2015 Medical Image Analysis 22:63-76
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Hot Spots Conjecture
Chung et al. 2011 Lecture Notes in Computer Science (LNCS). 7009:225-232
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Motivation: determine the length of mandible
First attempt: diffusion with heat source and sink
+ heat source
- heat sink
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Laplace-Beltrami eigenfunctions on mandible
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Rauch’s hot spots conjecture unsolved since 1974
�f
�t= �f, f(x, t = 0) = X(x)
@f
@n
(x, t) = 0
Neumann boundary conditionConjecture: The distance from the max/min of the solution to the boundary goes to zero as t goes to infinity.
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K� � f(p) =RM f(p) dµ(p)
µ(M)+ f1e
��1�⇥1(p) + R(�, p)
Asymptotic property of diffusion
Mean signal over manifold
Remainder
Restatement: The hot and cold spots of the second eigenfunction occur at the points that give the maximum geodesic distance.
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Properties of Fidler vector on graphs
Hilbert nodal line theorem: The Fiedler vector partitions the node set into exactly two sign domains.
Hot spot at the positive domain. Cold spot at the negative domain.
Tlusty 2007 (Electronic Journal of Linear Algebra): The hot/cold spots have to occur at the boundary.
1
4
2
3
0.419
-0.702
0.202
-0.338
50.419
-
+
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Example 3D surfaces
Tried more than 30 different objects: 2D, 3D trees, surfaces etc.
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Second eigenfunction on trees
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The topology/geometry aware 2nd eigenfunction
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Can you guess where the max. and min of the 2nd eigenfunction will occur here?
+ - -
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Applications
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Red= femaleBlue = male
Centerline of mandible for 70 subjects
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Elongation of mandible (length increases, angle decreases)
age
length
angle
children
adults
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Diameter:maximum of all possible shortest paths
Running time O(n(n+e)) Worst case O(n3)
Diameter of a large-scale network
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What next?
Persistent homology on hierarchical networks
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Dense brain networks (fMRI correlation)
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Hierarchical sparse network (compressed sensing)
S(λ)
Sparsity
Sparse modelwith sparseparameter :λ
λ = 0.3
λ = 0
λ = 0.5Directed graph:Chung et al. 2017 IPMI 10265:299-310
Chung et al. 2015 IEEE TMI 34:1928-1939Undirected graph:
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Standard brain parcellation with 116 regions
Precentral gyrus
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18-layer convolutional brain network
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19-layer hierarchical brain parcellation
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White matter fiber tracts
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19-layer convolutional brain network