4.1.1
Surface entropy
In this work, as presented in [4], a surface S ⊂Rd is sampled using a discrete set of
N surface points, Z = (X1, X2, . . . , XN). These surface points, called particles, are
considered to be random variables drawn from a probability density function (PDF),
p(X). A realization of this PDF is denoted with lowercase; thus, a particle set is represented by z = (x1, x2, . . . , xN), where z ∈ SN. The probability of a realization
x isp(X =x), which is denoted simply asp(x).
Cates et al. describe in [4] a nonparametric Parzen windowing method to estimate
p(xi) such that p(xi)≈ 1 N(N −1) N X j=1,j6=i G(xi−xj, σi). (4.1)
where G(xi − xj, σi) represents a d-dimensional isotropic Gaussian with standard
deviationσi. The value for σi is computed using Newton-Raphson method such that
∂p(xi, σi)/∂σi = 0, which optimizes the probability of the particle i being at the
position indicated by the current configuration.
The amount of information contained in such a random sampling is the differ- ential entropy of the PDF in the limit, which is H[X] = −R
Sp(x) logp(x)dx =
−E{logp(X)}, where E{·} denotes expected value. The cost function C is the neg- ative of this expected value, which can be approximated by the sample mean for a sufficiently large sample size. The optimization problem is thus given by:
ˆ z = arg min z C(z) s.t. x1, . . . , xN ∈ S (4.2) C(z) =−H[X]≈ N X i=1 log 1 N(N −1) N X j=1,j6=i G(xi−xj, σj) (4.3) −∂C ∂xi = 1 σ2 PN j=1,j6=i(xi−xj)G(xi−xj, σi) PN j=1,j6=iG(xi−xj, σi) =σi−2 N X j=1,j6=i (xi−xj)wij (4.4)
wherewij are weights such that
PN
j=1wij = 1. This can be interpreted as the particles
moving away from each other under a repulsive force while constrained to lie on the surface. The motion of each particle is away from all of the other particles, but the forces are weighted by a Gaussian function of inter-particle distance. Interactions are therefore local for sufficiently small σ. Furthermore, the Gaussian kernels are truncated at 3σ to ensure that each particle has a finite radius of influence.
Note that this particle formulation computes Euclidean distances between par- ticles rather than the geodesic distances on the surface. Thus, a sufficiently dense sampling is assumed, so that nearby particles lie in the tangent planes of the zero sets of a scalar function F which provides the implicit cortical surface. This is an
important consideration; in cases where this assumption is not valid, such as highly curved surfaces, the distribution of particles may be affected by neighbors that are outside of the true manifold neighborhood.
The preceding minimization produces auniformsampling of a surface. A strategy that samples adaptively in response to higher order shape information can be more effective for some applications. In particular, sampling high-curvature regions more densely can be desirable to ensure the validity of the assumption that tangent planes vary smoothly between neighboring particles. This can be achieved by modifiying Eq. 4.1 by introducing a scaling factor kj proportional to the root sum-of-squares of
the principal curvatures at each particle j:
p0(xi)≈ 1 N(N −1) N X j=1,j6=i G( 1 kj (xi−xj), σi). (4.5)
The human cortex is a prime example of a highly curved surface. In fact, due to the high level of convolution, even a strong degree of adaptivity does not produce a satisfactory sampling of these surfaces such that nearby particles can be assumed to lie on the local tangent planes, unless a very high number of particles are used. However, using such a high number of particles is undesirable due to computational cost. Therefore, in this work, I overcome this problem by inflating the cortical sur- face prior to optimizing correspondence instead of adaptive sampling. The particles therefore live in the tangent planes of the inflated surface; they are only pulled back to the original cortical surface for correspondence evaluation purposes.
For both uniform and adaptive sampling, the constraint of the particles to the object surface is achieved via an implicit representation where the surface is defined by the zero-set of a signed distance function F(x). This constraint is maintained at each iteration of the optimization, as described by Meyer et al. [50], by projecting the gradient of the cost function onto the tangent plane of the surface, moving the
particle along this tangent vector, and projecting the particle to the closest root of F by the Newton-Raphson method. The sampling system is initialized with a single particle at a root of F that is recursively split in two to produce the desired number of particles, as described by Cates [4].
4.1.2
Ensemble entropy
An ensembleE is a collection of M surfaces each with their own set of particles, i.e., E =z1, . . . , zM. The ordering of the particles on each shape implies a correspondence
among shapes and the entire population can be represented in a matrix of particle positionsP =xkj with particle positions along the rows and shapes across the columns. Cates et al. [3] model zk ∈ RN d as an instance of a random variable Z, and they
propose to minimize the combined ensemble and shape cost function
Q=H(Z)−
M
X
k=1
H(Pk), (4.6)
which favors a compact ensemble representation balanced against a uniform (or adap- tive) distribution of particles on each surface.
For this discussion, the complexity of each object is assumed to be greater than the number of objects, and therefore it is typical to have N > M. Given the low number of examples relative to the dimensionality of the space, some conditions must be imposed in order to perform the density estimation. Cates assumes a normal distribution and models the distribution of Z parametrically using a Gaussian with covariance Σ. The entropy of a g-dimensional Gaussian with covariance Σ can be computed as H =− Z Z . . . Z f(x)ln(f(x))dx (4.7) = 1 2(g+gln(2π) +ln|Σ|) (4.8)
The ensemble entropy can therefore be expressed as H(Z)≈ 1 2log|Σ|= 1 2 N d X j=1 logλj, (4.9)
where λ1, ..., λN d are the eigenvalues of Σ.
In practice Σ will not have full rank, in which case the entropy is not finite. It is therefore necessary to regularize the problem with the addition of a diagonal matrix αI to introduce a lower bound on the eigenvalues. Let Y = P −P, where
P is the matrix that encodes the vertex locations for each object along successive columns, P is a matrix with all columns set to the mean shape µ. The covariance can then be estimated from the data, with Σ = (1/(M −1))Y YT. Because N > M,
the computations are performed on the dual space (dimension M rather than N), knowing that the determinant is the same up to a constant factor of α. Thus, the cost function G associated with the ensemble entropy is defined as:
log|Σ| ≈G(P) = log 1 M−1Y TY (4.10) and −∂G ∂P =Y(Y TY +αI)−1. (4.11)
It is now clear that α is a regularization on the inverse of YTY to account for the
possibility of a diminishing determinant. Starting with a large α and incrementally reducing it as the optimization converges yields an annealing approach which improves computational efficiency; this has the effect of preventing the system from attempting to reduce the thinnest dimensions of the ensemble distribution too early in the process. The negative gradient −∂G/∂P gives a vector of updates for the entire system, which is recomputed once per system update. This term is added to the shape-based
updates described in the previous section to give the update of each particle:
xkj ←γ−∂G/∂xjk+∂Ck/∂xkj. (4.12)