US7177486B2  Dual bootstrap iterative closest point method and algorithm for image registration  Google Patents
Dual bootstrap iterative closest point method and algorithm for image registration Download PDFInfo
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 US7177486B2 US7177486B2 US10/408,927 US40892703A US7177486B2 US 7177486 B2 US7177486 B2 US 7177486B2 US 40892703 A US40892703 A US 40892703A US 7177486 B2 US7177486 B2 US 7177486B2
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Classifications

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T3/00—Geometric image transformation in the plane of the image
 G06T3/0068—Geometric image transformation in the plane of the image for image registration, e.g. elastic snapping
 G06T3/0081—Geometric image transformation in the plane of the image for image registration, e.g. elastic snapping by elastic snapping

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T7/00—Image analysis
 G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
 G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using featurebased methods

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T2207/00—Indexing scheme for image analysis or image enhancement
 G06T2207/30—Subject of image; Context of image processing
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Abstract
Description

 The DualBootstrap ICP has the major advantage requiring fewer initial correspondences than with existing methods. This is because DualBootstrap ICP starts from an initial loworder transformation that need be accurate in only small initial regions.
 Instead of matching globally, which could require simultaneous consideration of multiple matches, DualBootstrap ICP uses region and model bootstrapping to resolve matching ambiguities.
 DualBootstrap ICP improves upon current landmarkbased retinal image registration algorithms by requiring many fewer landmark correspondences. Compared to intensitybased methods, it uses the dualbootstrapping procedure to avoid the ambiguities that cause other algorithm to use expensive global search techniques.
TABLE 1 
Transformation Models. DoF stands for Degrees of Freedom 
Transformation  Equation  DoF  Accuracy 
Similarity 

4  5.05 pixels 
Affine 

6  4.58 pixels 
Reduced quadratic 

6  2.41 pixels 
Full quadratic 

12  0.64 pixels 

 Let I_{1 }be the image being mapped and I_{2 }be the fixed image that I_{1 }is being mapped onto. The terms “image” and “image plane” have the same meaning herein.
 R_{t }denotes the bootstrap region at iteration t of the dualbootstrap procedure. Bootstrap regions are defined in the coordinate system of image I_{1}. The initial bootstrap region is R_{1}.
 M(θ; p) is a transformation function mapping image location (or feature vector) p from the coordinate system of I_{1 }onto the coordinate system of I_{2}. Here θ is the set of transformation parameters to be estimated.
 Let M be a sequence of such transformation functions or “models”. The model set may or may not form a nested hierarchy. In the retina application, M is {similarity, affine, reducedquadratic, full quadratic} (Table 1).
 M_{t }denotes the model selected in bootstrap region R_{t }during iteration t. {circumflex over (θ)}_{t }is the estimated vector of parameters instantiating the model M_{t}.
 Σ_{t }is the covariance matrix of the estimated parameter vector {circumflex over (θ)}_{t}.
 E(R_{t}, M_{t}, θ_{t}) is the registration objective function that measures the transformation error between I_{1 }and I_{2 }in region R_{t }using model M_{t }and parameter vector θ_{t}.
3.2 Procedure
The description of the DualBootstrap ICP procedure is based on a single initial transformation estimate and associated bootstrap region. Generally, one or more initial estimates will be evaluated, each triggering a separate application of the procedure.  1. Establish the initial bootstrap region R_{1 }in a small area around where the initial estimate is computed, and initialize model M_{1 }to be the lowest order model.
 2. Set iteration index t=1;
 3. While the estimate has not converged:
 (a) Estimate parameter vector {circumflex over (θ)}_{t }by minimizing E(R_{t}, M_{t}, θ_{t}). Calculate the covariance matrix Σ_{t }of the estimate {circumflex over (θ)}_{t}.
 (b) Bootstrap the model: apply a model selection technique to choose the new model M_{t+1}. If the model selection technique chooses a new model—that is, M_{t}≠M_{t+1}—then {circumflex over (θ)}_{t }and Σ_{t }must be replaced by the estimate and covariance computed for M_{t+1 }during model selection.
 (c) Bootstrap the region: use the covariance matrix Σ_{t}, and the new model M_{t+1}, to expand the region based on the “transfer error”. The growth rate is inversely related to this error.
 (d) Check for convergence. This occurs when R_{t }reaches the apparent region of overlap between images and minimization of E(R_{t},M_{t}, θ_{t}) converges within R_{t}.
 (e) Increment iteration index t by 1 (t=t+1)
Σ_{t}={circumflex over (σ)}^{2} H ^{−1}(E(R _{r} ,M _{t},{circumflex over (θ)}_{t})) (1)
wherein {circumflex over (σ)}^{2 }is the estimated variance of the alignment error.
where d is the degrees of freedom in the model,
is the sum of the robustlyweighted alignment errors (based on the estimate {circumflex over (θ)}), and det(Σ) is the determinant of the parameter estimate covariance matrix Σ. Note that the t subscripts have been dropped in Equation (2). If Σ_{θ} is not fullrank, then the third term (i.e., log det(Σ)) goes to −∞. For techniques having no explicit measure of alignment error a different measure, other than
will be needed.
Equation (3) expresses the transfer error.
This growth Δp_{c}, in normal direction η_{c}, is proportional to the current distance (p_{c} ^{T}η_{c}) of the side p_{c }lies on from the center of R_{t }and is inversely proportional to the transfer error in the normal direction. The lower bound of 1 in the denominator prevents growth from becoming too fast. Each side of R_{t }is grown independently using Equation (4). Parameter β tunes the growth rate. A value of β=√{square root over (2)}−1 ensures that the area of a twodimensional region at most doubles in each iteration. Generally β should be in a range of 1 to 8. More sophisticated growth strategies are certainly possible, but the one just described has proven to be effective.
Details of this objective function are described as follows.

 The minimization is restricted to the set of points in P that are also in bootstrap region, R_{t}.
 d(M_{t}(θ_{t};p),I_{2}) is the distance between the transformation of p and the representation of I_{2}, where p represents a vector of points in I_{1}. The distance metric depends on the types of point vectors, p. For detected comer points, interest points or other image landmarks, the natural metric is the Euclidean distance. See
FIG. 7 described infra for a discussion of landmarks. For points that are samples from smooth regions of curves or surfaces, pointtoline or pointtoplane normal distances are generally more appropriate (also, seeFIG. 7 described infra).  ρ(u) is a robust loss function, monotonically nondecreasing as a function of u. A leastsquares loss function is obtained by making ρ(u)=u^{2}, but because mismatches are common, robust loss functions are crucial.
 {circumflex over (σ)} is the error scale, which is the (robust) standard deviation of the error distances.
Equation (6) may be minimized with respect to the transformation parameters {circumflex over (θ)}_{t }using iterativelyreweighted leastsquares (IRLS), with weight function w(u)=ρ′(u)/u. The minimization process alternates weight recalculation using a fixed parameter estimate with weighted leastsquares estimation of the parameters.
4.3 Robust Error Scale Estimation
where C(k,N) is a computed correction factor. This factor makes σ_{k} ^{2 }an unbiased estimate of the variance of a normal distribution using only the first k out of N errors. The intuition behind MUSE is seen by considering the effect of outliers on σ_{k} ^{2}. When k is large enough to start to include outliers (errors from incorrect matches), values of σ_{k} ^{2 }start to increase substantially. When k is small enough to include only inliers, σ_{k} ^{2 }is small and approximately constant. Thus, the algorithm can simply evaluate σ_{k} ^{2 }for a range of values of k (e.g. 0.35N, 0.40N, . . . , 0.95N), and choose the smallest σ_{k} ^{2}. To avoid values of k that are too small, the algorithm may take the minimum variance value of σ_{k} ^{2}, not just the smallest σ_{k} ^{2}.
5. Retinal Image Registration Using DualBootstrap ICP
d(M _{t}(θ_{t} ;p _{i}),q _{j})=(M _{t}(θ_{t} ;p _{i})−q _{j})^{T}η_{j}
TABLE 2  
Success Rate of Retinal Image  
Registration (%) 
All Pairs  One Landmark Pair  
Healthy  S_{h }(%)  97.0  99.5  
Pathology  S_{p }(%)  97.8  100  

 Using matching of single landmarks between images resulted in a 96.7% success rate, whereas matching pairs of landmarks from each image resulted in a 90.4% success rate. Since the overall performance was 97.0%, the combination of both did improve performance, although single landmark matching alone was nearly as effective.
 Over the entire dataset, including both healthy and pathology eye images, the median number of matches tried before the algorithm succeeded was 1 and the average was 5.5. The large difference between the median and the average is caused by a small number of image pairs that required an extremely large number of initial estimates before success. The worst was 746.
 The execution time required by the algorithm varied considerably with the number of initial estimates required before success. On a 933 MHz Pentium III computer running FreeBSD, the median time was 5 seconds.
 In preliminary experiments on multimodal registration of redfree images and fluorescein angiograms, the DualBootstrap ICP has nearly the same performance as the results reported. It fails for extreme cases of retinas where leakage of the dye from the blood vessels is immediate and completely obscures the vasculature, or for angiograms taken long after the injection of the dye, when the dye has slowly perfused the retina. In both cases, failure is largely due to a lack of initial landmarks.
FIG. 2 , discussed supra, shows an example of multimodal registration.
6.3 Evaluation of the DualBootstrap Procedure
TABLE 3  
Success Rate of Retinal Image Registration (%) 
No  No  No  
Region Growth  Model Selection  Robustness  
Healthy  S_{h }(%)  89.4  84.7  39.0 
Pathology  S_{p }(%)  82.4  80.5  12.5 

 Deciding when it is needed requires analyzing the combination of modeling error and transfer error of initial estimates. If this error, imagewide, is at least half the distance between the structures (e.g. blood vessels) alignment is based on, then the dualbootstrap approach is needed. Interestingly, this question can be reversed to ask if, by using the dualbootstrap approach, the initial conditions required for registration can be weakened and therefore more difficult problems addressed.
 Other than the obvious case of no valid initial correspondences, the dualbootstrap approach can fail in two ways. The first is when the initial model is too weak. One example might be use of an initial rigid transformation when significant scaling is present. The second way it can fail is when the images contain two geometricallyseparated clusters of features, and the initial transformation is estimated only in a single cluster. As the gap between clusters grows, the transfer error will grow with it, potentially leading to mismatches when the bootstrap region R_{t }grows to include a second cluster.
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