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Scatter Search for the Point Matching Problem in 3D Image Registration

Scatter Search for the Point Matching Problem in 3D Image Registration
Oscar Cordón
Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Daniel Saucedo Aranda s/n, 18071 Granada, Spain, ocordon@decsai.ugr.es European Centre for Soft Computing. Edificio Científico-Tecnológico, C/ Gonzalo Gutiérrez Quirós, s/n, 33600 Mieres (Asturias), Spain, oscar.cordon@softcomputing.es

Sergio Damas
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, Daniel Saucedo Aranda s/n, 18071 Granada, Spain, sdamas@ugr.es European Centre for Soft Computing. Edificio Científico-Tecnológico, C/ Gonzalo Gutiérrez Quirós, s/n, 33600 Mieres (Asturias), Spain, sergio.damas@softcomputing.es

Jose Santamaría
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Cádiz, Chile 1, 11002 Cádiz, Spain, jose.santamarialopez@uca.es

Rafael Martí
Departamento de Estadística e I.O., Universidad de Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain, rafael.marti@uv.es

Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. We present a scatter-search implementation designed to find high-quality solutions for the 3D image-registration problem, which has many practical applications. This problem arises in computer vision applications when finding a correspondence or transformation between two computer images obtained under different conditions. Our implementation goes beyond a simple exercise on applying scatter search, by incorporating innovative mechanisms to combine and improve solutions and to create a balance between intensification and diversification in the reference set. Furthermore, heuristic information taken from a preprocessing of the images is incorporated into the algorithm to improve its performance. Our computational experimentation tackling two different medical registration applications establishes the effectiveness of scatter search in relation to different approaches usually applied to solving the problem. We have considered both simulated magnetic resonance images and real-world computerized


tomography images as datasets. To measure the robustness of our proposal, the image datasets are intentionally selected for addressing registration environments with the presence of noise, anatomical lesions and occlusions between images. Key words: metaheuristics; evolutionary computation; scatter search; image registration; point matching

1. Introduction
The purpose of this paper is to develop a heuristic method for solving an important combinatorial optimization problem. Specifically, we tackle the 3D image-registration (IR) problem in the context of computer vision systems (Brown 1992). The practical applications of IR are numerous and they include 3D model construction (Eisert et al. 2000), autoradiograph alignment in neuroscience (Rangarajan et al. 1997) or statistical physics (Yuille and Kosowsky 1994). The main contribution of our work is the development of a procedure based on scatter-search (SS) methodology (Glover 1977, 1998), which is used for searching the solution space of the optimization problem that appears in the IR process. The proposed SS-based IR algorithm is a significantly improved version of that in Cordón et al. 2004, which allows us to deal with more complex IR problems properly (see Section 4). In particular, innovative mechanisms to exploit the knowledge of the problem and to create a trade-off between intensification and diversification for an efficient search are incorporated (a restricted version of this method can be found in Cordón et al. 2005). IR can be simply defined as finding a mapping between two images: I1 named scene, and I2 named model. The objective is to find the mathematical transformation f that, applied to I1, obtains I2. Generally speaking, an image is stored in a huge number of pixels, so most IR methods usually apply preprocessing to extract the most relevant geometric primitives (points, lines, etc.) that, in a certain way, define the objects contained in the image. Therefore, in these feature-based methods, the problem is reduced to finding the transformation between two sets of geometric primitives. In this paper, we restrict our attention to the case of two sets of primitives P1 and P2, consisting uniquely of points (P1 ? I1, P2 ? I2). Hence, this IR problem can be defined in two different search spaces (both with the same final goal of achieving the best alignment between the scene and model images): the space of parameters that define f, or the space

of permutations of P1 (to match P2). While the former approach to solving the problem is based on directly searching for the best parameters defining the transformation f (see, for example, Yamany et al. 1999), the latter, called point matching, is probably the most classical method in feature-based registration. Point matching can be described in mathematical terms as follows. Given two set of points P1={x1, x2,..., xn} and P2={y1, y2,..., ym}, the problem is to find a transformation f such that yi=f(xσ(i)) for i=1,…, r, where r=min(n,m) and σ is a permutation of size l (with l being the maximum between n and m). The IR problem is then naturally divided into two phases. In the first one, a permutation σ of l elements defines the matching between the points in P1 and P2. In the second phase, from this matching of points and using a numerical optimization method (usually a least-squares estimation), the parameters defining the transformation fσ are computed. The objective is to find the transformation minimizing the distances between the model points and the corresponding transformed scene points. Therefore, in optimization terms, the value associated with permutation σ is given by:

g (σ ) =

i =1



f σ ( xσ (i ) ) ? y i r



The point-matching problem can be simply stated as minimizing g(σ) for any permutation σ of l elements and its corresponding transformation fσ. We face the IR problem within this point-matching approach, as do previous methods like the wellknown iterative closest point algorithm (Besl 1992, Feldmar and Ayache 1996, Liu 2004), the technique usually applied in computer vision. We propose an SS implementation to find high-quality solutions for this combinatorial optimization problem. Our solution method presents contributions in both optimization and IR. Evolutionary methods have been widely applied to solving IR problems and typically use genetic operators for combination (Matsopoulos et al. 1999, Yamany et al. 1999, Rouet et al. 2000, He and Narayana 2002, Chow et al. 2004, Wachowiak et al. 2004). On the other hand, SS (Glover 1977, Laguna and Marti 2003) is based on a systematic combination between solutions (instead of a randomized one like that usually employed in genetic algorithms (GAs)) taken from a considerably reduced evolved pool of solutions called the reference set (between five and ten times lower than the usual GA


population sizes). In this way, an efficient and accurate search process is encouraged for the IR problem in this paper thanks to the latter and to other innovative components that will be described later. Likewise, we test the effectiveness of other combination mechanisms that do not rely on randomization in the context of point matching. Moreover, we design problem-dependent search mechanisms based on image-specific information, which have been proved to return good-quality solutions (Cordón and Damas 2006). This information is a priori extracted from the shapes of the objects existing in the images and results in the association of heuristic values to each image point. This information is used in a two-fold way: on the one hand, the differences between the heuristic values of the matched points in the current solution are incorporated into the solution evaluation better to guide the search from a global perspective. On the other hand, they are taken into account in the neighborhood operator of the local search mechanism to intensify the search properly, as well as in the diversification-generation method to create an initial set of high-quality solutions with a large degree of diversity among them. In this way, we implement candidate-list strategies in which permutations assigning feature points with similar heuristic values are ranked first, since they seem more promising than do those with relatively different values. The consideration of this additional information in the point-matching process allows the SS algorithm to obtain high-quality solutions more quickly than do other previous approaches. We first briefly describe SS methodology and then, in Section 3, we present our implementation to solve the point-matching problem. The paper ends with the computational experiments and associated conclusions.

2. Scatter Search
Scatter search was first introduced in Glover (1977) as a heuristic for integer programming. SS orients its explorations systematically relative to a set of reference points that typically consist of good solutions obtained by prior problem-solving efforts. The SS template (Glover 1998) has served as the main reference for most of the SS implementations to date. SS methodology is very flexible, since each of its components can be implemented in a variety of ways and degrees of sophistication. In the Online Supplement to this paper on the journal's web site we give a description to implement SS based on the well-known “five-method template” (Laguna and Martí 2003). The


advanced features of SS are related to the way these five methods are implemented. That is, the sophistication comes from the implementation of the SS methods instead of the decision to include or exclude certain elements (as in the case of tabu search or other metaheuristics).

3. The Point-Matching Search Method
As described on the supplement, SS methodology basically consists of five elements (and their associated strategies). Three of them, the diversification-generation, the improvement and the combination methods, are problem-dependent and should be designed specifically for the problem at hand (although it is possible to design “generic” procedures, it is more effective to base the design on the specific conditions of the problem setting). The other two, the reference-set-update and the subset-generation methods are context-independent, and usually have a standard implementation. We consider the same design of the preliminary version (Cordón et al. 2004) for the two context-independent components. However, the implementation of two of the three specific elements (the diversification-generation method and the improvement method) has been changed to improve the performance of our SS-based IR technique, allowing it to deal with significantly more complex problem instances. We have implemented an advanced design of the reference set that complements the RefSet creation mechanism introduced in the supplement by an updating process that proactively injects diversification into the search. This strategy is called a 2-tier design (Laguna and Martí 2003) and is based on partitioning the RefSet into two tiers. The first tier, RefSet1 (Quality RefSet), consists of b1 high-quality solutions {S 1 ,..., S b1 } , while the second tier, RefSet2 (Diversity RefSet), consists of b2=b-b1 diverse solutions {S b1 +1 ,..., S b2 } . The solutions in RefSet1 are ordered according to their objective function and a new solution S replaces the worst solution S b1 if the quality of the former is better than that of the latter. RefSet2 is ordered according to the diversity value of the solutions, so a new solution S replaces the worst solution
S b2

if d ( S ) > d ( S b2 ) , where the diversity value d is

computed with the Point Matching Distance defined in Section 3.2. Following the guidelines given in Laguna and Martí (2003), we implement the combination and the subset-generation methods with all pairs (2-element subsets) in the RefSet with a static updating. As our reference set is composed of a quality and a diversity part, solution subsets of three different kinds are generated. On the one hand,


subsets with the b1(b1-1) possible pairs of solutions in the quality RefSet are created to intensify the search by combining high-quality solutions. On the other hand, each of the b2(b2-1) pairs of solutions in the diversity part are also considered to generate combined solutions for diversification purposes. Finally, a third group of b1b2 subsets is created by pairing each solution of the quality part with each one in the diversity part, thus getting combined solutions with an intermediate search behavior. Every new trial solution generated in the combination and improvement steps is inserted into a pool of solutions, Pool, and decreasingly ordered according to their objective-function value. Those worst solutions in RefSet1 will be replaced by the corresponding better solutions in Pool. Subsequently, the remaining solutions in Pool (all of them with a lower quality than those in RefSet1) will be considered to update RefSet2. The next four subsections are respectively devoted to describing the coding scheme and the use of image-curvature information in our search method, and the three specific SS elements mentioned above: the diversification-generation method, improvement method and solution combination method.

3.1 . Image-Curvature Information and Coding Scheme
Our proposal is based on solving the IR problem by searching in the feature-based matching space, so a coding scheme specifying the matching between model and scene image primitives (points, in our case) has to be defined. First, a pre-processing step (a 3D crest lines edge detector (Monga et al. 1991)) is applied to extract the most relevant feature points for each image, P1={x1, x2,..., xn} for the scene and P2={y1, y2,..., ym} for the model. We first compute the iso-surface of the 3D image (i.e. the surface that separates regions of the space when considering a given intensity value known as the iso-value). The goal is to obtain the boundary of the object under study (brain, liver, skull, etc.), from an image that typically stores different shapes. This surface defines, for any point x in the image, a set of curvatures C(x) reflecting the variation from x in each direction with respect to the tangent plane at this point. Hence, iso-surfaces allow us to reduce the huge amount of data with which we are dealing. If we focus our attention on the zerocrossings of the curvature function C(x), such points (known as crest-line points) correspond to ridges and valleys of the iso-surface and represent its most important features. Thanks to this preprocessing, instead of facing the point-matching problem


from a million-size permutation, we take advantage of curvature information to extract the most relevant points in the image and face a hundred-size permutation problem. The point matching between both images is represented as a permutation σ = (σ1,

σ2, ..., σl) of size l=max(n,m), which associates the r points (r=min(n,m)) of the smaller
size point set to the first r points of the permutation, selected from the larger one. Without loss of generality, and to simplify the notation, we consider that P1 is larger than or equal to P2 (n ≥ m). We have implemented our solution method in such a way that the first r elements of the permutation (r=m in our case) are the P1 points associated to each of the m points in P2. Figure 1 illustrates these implementation details in which we can see that the P1 points located between positions m+1 and n are not assigned to any point in P2. Meanwhile, the first m points of the permutation define a matching between the smaller point set P2 (of size m) and the larger one P1 (of size n); i.e., σ(20)=45, defines a matching between the 20th point of P2 and the 45th of P1 (with 20≤ m and 45 ≤ n).

Figure 1: Implementation Details of the Point-Matching Permutation σ with Size n

Then, we are able to infer the parameters of the implicit registration transformation f existing between the two 3D images, fσ, from the point matching σ by means of simple numerical methods such as the closed-form solution based on unit quaternion (Horn 1987) solving a least-squares problem. We consider f to be a similarity transformation, thus being composed of a rotation R=(λ,<φx,φy,φz>), a translation t=(tx,ty,tz), and a uniform scaling s. Such a transformation can be used to register aerial and satellite images, bony structures in medical images, and multimodal brain images (Goshtasby 2005).


Once we know the expression of fσ, i.e. the (R,t,s) parameters defining the similarity transformation, we can estimate the registration error existing between the scene image points xi and the model image points yi, measured by the g() function as proposed by Arun et al. (1987). We estimate the registration error by simply computing the Euclidean distance from each transformed point in P1 (using the aforementioned fσ parameters) to its corresponding matching (considering σ ), as shown in (2):

g(σ) =

i =1

f σ ( xσ (i ) ) ? y i r


, where f σ ( xσ (i ) ) = s·R( xσ (i ) ) + t


Note that g(σ)computes only the geometric information of both scene and model feature points. Some authors (Yamany et al. 1999, Luck et al. 2000, Robertson and Fisher 2002) have proposed several metaheuristic approaches that are aimed only at minimizing the previous g(σ) error function. However, by considering only this objective evaluation function, search algorithms suffer from several problems such as their inability to handle large initial misalignments between the two images, and those situations where the images have rotational or translational symmetries, both due to the fact of dealing only with the object geometry (Gagnon et al. 1994, Weik 1997). The latter aspects usually make the given IR algorithm more likely to become trapped in local optima. A good explanation of such undesirable behavior is found in Luck et al. (2000), who use a simulated annealing method to address these problems. To overcome the latter problems in our SS-based IR procedure, we make use of problem-dependent (context) information in the search method. To do so, we again take into account the curvature information C(x) extracted by the 3D crest lines edge detector. For each point x, we consider the two values of the first and second principal curvatures, k1(x) and k2(x) in C(x), associated with the two principal orthogonal directions (which locally characterize the iso-surface). An interesting quality of this feature is that curvature values represent an invariant source of information with respect to the similarity transformation fσ with which we are dealing, i.e. for each point x, we have that k1(x)=k1(fσ(x)) and k2(x)=k2(fσ(x)). The curvature attributes remain unchanged although a different fσ is applied. Therefore, given a scene point xi and a model point yj (each described by two curvature values), the closer every pair of curvature values, the higher the probability of a good matching between xi and yj. Therefore, we introduce the matrix D=(dij)n×m to


store all the Euclidean distances between the curvature values of each scene and model point. In mathematical terms,
dij =

(k1( xi ) ? k1( y j ))2 + (k2 ( xi ) ? k2 ( y j ))2 ,

?xi ∈ P 1, y j ∈ P 2.


We will use these distances between curvature values in both a diversification generation method and an improvement method of our SS procedure. In the former, they will be considered for an alternative solution evaluation, while in the latter they restrict the size of the neighborhood of a given solution for an efficient search. In both cases, this curvature information avoids the aforementioned problems, as will be shown in Section 4.

3.2. Diversification Generation and Reference-Set Construction
Instead of considering a completely random generation of the initial solutions as in Cordón et al. (2004), we use the heuristic information related to image curvature described in Section 3.1 to establish a preference for good assignments between the scene image points and the model image ones. Hence, a point xi from the scene image is more likely to be assigned to those model points yj presenting the same or similar curvature (heuristic) values k1 and k2, i.e. having the lower distances dij. We can make use of this information to generate the initial set P of diverse solutions for our SS procedure, thus obtaining solutions of both good quality and high diversity. Specifically, instead of fixing a selection order for the scene points xi and then assigning the closest model point yj (with regard to the curvature values) not yet considered to each of them (which would result in a deterministic, greedy heuristic), we introduce randomness into both processes, allowing each decision to be randomly taken among the best candidates. In this way, our diversification generation behaves similarly to a GRASP construction phase (Resende and Ribeiro 2001). The most important element in this kind of construction is that the selection in each step must be guided by a greedy function that adapts according to the pseudo-random selections made in the previous steps. Our method starts by creating two candidate lists of unassigned points (CL1 and CL2) that, at the beginning, consist of all the points in the scene and the model (i.e. initially CL1 = P1 and CL2 = P2). For each element xi in CL1, we compute its potential distance di to CL2 as the minimum value of the distances from xi to all the elements in CL2. Then, we construct the restricted candidate list RCL1 with a percentage α of the


elements in CL1 with the lowest di-values, and we randomly select one element (say xk) from RCL1 for the matching assignment. To find an appropriate point in the model to match with xk, we construct the restricted candidate list RCL2 with a percentage α of the elements in CL2 whose curvature values are closer to those of xk, i.e. those elements presenting the lowest distance values to xk. Finally, we randomly select a point (say yk) in RCL2 and match it with xk. We update CL1 and CL2 (CL1=CL1 – {xk}, CL2=CL2 – {yk}) and perform a new iteration. The algorithm finishes when r=min(n,m) points have been matched, i.e. when either CL1 or CL2 (the one corresponding to the image with the fewest points associated) becomes empty, and the remaining l-r points of the permutation are taken from the points still stored in the non-empty candidate list in a random order. We repeat the application of this pseudo-random construction algorithm until we obtain |P| different solutions. We then apply the improvement method below to the solutions generated. Since two different solutions can produce the same improved solution we apply the construction step a number of extra times, if necessary, until |P| different improved solutions are obtained. Let P be the set of these improved solutions. As mentioned above, the reference set, RefSet, is a collection of b solutions (reference points) that are used to generate new solutions. The construction of the initial reference set starts with the selection of the best b1<b improved solutions from P. These solutions are added to RefSet and deleted from P. The remaining b2=b-b1 RefSet solutions are selected from P, taking into account the diversity. To do so, there is a need to define a distance metric between the solution vectors, i.e. between permutations. We consider the distance between two permutations σ = (σ1, σ2, ..., σl) and ρ = (ρ1, ρ2, ...,

ρl) as the number of times σi differs from ρi for i = 1, …, r. Additionally, in order to
favor the inclusion of quality solutions, as measured by the objective function, we bias the distance measure and divide this quantity by the sum of the evaluations of both solutions modified according to the curvature values. We call this metric the pointmatching distance (PMD) to differentiate it from the point-curvature distance d and its definition in mathematical terms is:

PMD(σ , ρ ) =

∑ min(1, σ
i =1



? ρi )

F (σ ) + F ( ρ )




In this expression, we use an alternative solution evaluation F(σ) that incorporates the distance between curvature values to overcome the limitations of the objectivefunction evaluation shown in the section 3.1. Specifically, the value of F(σ) is given by:
F (σ ) = wg ·g (σ ) + wnerror ·merror (σ )

with merror (σ ) =

∑d σ
i =1


2 i (i )



where the error function merror(σ) measures the goodness of the matching σ by using the extra curvature-information attributes associated with each feature point, and the weights wg and wnerror define the relative importance of each term. With such a function, we will have a more suitable similarity measure to make a better search in the solution space, addressing the drawbacks of the previous IR methods. Furthermore, this definition of the merror(σ) function is a specific case based on just two curvature values. Depending on the nature of the images considered, different attributes extracted in the IR pre-processing step can be considered for easy redefinition of the merror(σ) function as a reusability mechanism for other IR environments. Finally, the minimum PMD from each improved solution in P-RefSet to the current solutions in RefSet is computed. The solution with the maximum of these minimum distances is then selected. This solution is added to RefSet and deleted from P, and the minimum distances are updated. This process is repeated b2 times. As a result of the previous procedure, the reference set obtained has b1 high-quality solutions and b2 diverse solutions.

3.3. Improvement Method
Swaps are used as the primary mechanism to move from one solution to another in our improvement method. We define move(σi, σj), i∈{1, …, r=min(n,m)}, j∈{1, …,

l=max(n,m)}, j≠i, as consisting of exchanging σi and σj in the current solution σ. This
operation results in the ordering σ’= (σ1, …, σi-1, σj, σi+1, …, σj-1, σi, σj+1, …, σl) when

i<j (and symmetrically when j<i).
An important difference with other combinatorial optimization problems is that we cannot efficiently compute the move value associated with a trial move. In other words, to evaluate the quality of a move, we need to evaluate the final solution σ’ when the move is applied, and compare its value with that of the initial solution σ (move

value=g(σ)-g(σ’)). Note that a modification in the solution (permutation σ) means a
change in the matching and it implies a new estimated transformation f. Unfortunately,


the simple modification performed by the swapping of the matching of two points can result in a completely different registration transformation fσ’. Therefore, all the terms in the expression g(σ) can change and there is no way of calculating g(σ’) without computing the new transformation fσ’ and the corresponding transformed scene points. In Cordón et al. (2004), two improvements were considered to speed up the local search procedure. On the one hand, a primary strategy was applied in the neighborhood generation by considering only promising swapping moves that take the curvature information as a base. On the other hand, a selective application of the local optimizer was also considered. Both are explained in detail in the remainder of this section. A solution σ represents the matching (xσ(i), yi), for i=1,…,r. It is then to be expected that, in a good matching, points xσ(i) and yi will have similar curvature values. In mathematical terms, dσ(i)

should be relatively low for i=1,…,r. Since the move

evaluation is a relatively time-consuming operation, we reduce the neighborhood of a solution to include only promising moves. Specifically, the neighborhood of a solution

σ, N(σ), is restricted to those moves, move(σi, σj), in which this difference of curvatures
decreases for xσ(i) or xσ(j):

N(σ)={move(σi, σj) / dσ(j) i ≤ dσ(i) i or dσ(i) j ≤ dσ(j) j , 1≤ i≤ r, 1≤ j≤ l, j≠i}
solution contributes to the solution evaluation g(σ) in δi, where
δ i = f σ ( xσ (i ) ) ? yi


Given a solution σ and its associated transformation fσ, each element σi in the



This measure shows that points should not be treated equally by a procedure that selects an index for a local search (i.e. for search intensification). We consider that δ is a measure of influence and can be used to guide an efficient search of N(σ). Specifically, we order the elements in a solution according to their δ value and select the element σi* with the largest value for swapping. We then scan N(σ) (in the order given by the curvature distance dσ(i*)j) in search of the first element σj whose swapping move(σi*, σj) results in a strictly positive move value (i.e. a move such that g(σ’)≤g(σ)). As documented in Laguna et al. (1999), the first strategy does not necessarily select the best available solution in the neighborhood but after several iterations it can lead the search to a better solution than a greedy strategy based on the selection of the best solution at each iteration. If we do not find any improvement move associated to element σi*, we resort to the next one in the ordered list and proceed in the same way. The local search


method terminates either when N(σ) does not contain any improvement move or when a maximum number of iterations is reached. Computing the δ value, ordering the elements and selecting the most influential one is a computationally expensive calculation. To speed up our neighborhood operator, these δ values are not updated after the execution of a move at each local search iteration but, on the contrary, we keep the order as it stands and select the next element on the list for the next iteration (and proceed in the same way for a certain number of subsequent iterations). The notion of not updating key values (e.g. move values) after each iteration is based on the elite candidate list suggested by Glover and Laguna (1997). The design considers that it is not absolutely necessary to update the value of the moves in a candidate list after an iteration has been completed (i.e. the selected move has been executed) because most of these move values either remain the same or their relative merit remains almost unchanged. Application of this strategy is particularly useful when the updating of the move values is computationally expensive, as in our case. After k local search iterations, we update the δ-values and compute the new order. The parameter k reflects the trade-off point between information accuracy and computational effort in the implementation, and will be set after experimentation. Finally, a selective application of the local optimizer described above is also considered to speed up the whole SS procedure. Previous studies have demonstrated that a selective application of the local optimizer, with a random choice based on a given, low probability, has resulted in good performance in different memetic algorithms and, specifically, in some SS implementations (Hart 1994, Lozano et al. 2004, Herrera et al. 2006). In our case, this decision is deterministically taken, as the combined solution is optimized only when its evaluation F is better than that of at least one of the two original solutions used to generate it by the solution-combination method.

3.4. Solution-Combination Method
We have considered two types of combination methods, both of which generate a single combined solution from a subset composed of a pair of original solutions. The first, called partially mapped crossover (PMX), is based on random elements and is widely used in the context of GAs. The second one, called the voting method, is based on deterministic elements and is widely used in the context of adaptive memory

programming algorithms. We will compare both types of combinations in our computational experiments section. 3.4.1. Partially Mapped Crossover This is an implementation of the classical recombination operator for order-based representations called (PMX) (Goldberg and Lingle 1985). It is designed to preserve the absolute position of some elements in the first solution. The method randomly chooses two crossover points in one reference solution and copies the partial permutation between them into the new trial solution. Both crossover points also define a mapping between the elements in both reference solutions. The remaining elements are copied in the positions in which they appear in the second reference solution. If one position is already occupied by an element copied from the first parent, the element provided by the mapping is copied. This process is iterated until the conflict is solved. To limit the randomness of the method and to insure the contribution of both reference solutions to the new trial solution, we randomly generate the first crossover point cp1 in {1,0.5l} (assuming that 0.5l<r) and set the second crossover point cp2 to cp2= cp1+0.25l. As stated by Cotta and Troya (1998), this is a respectful operator since it transmits a relevant number of features from the original solutions to the combined solution. In genetic terms, we say that PMX transmits a block forma (an equivalence class induced by the relations identified as relevant). These authors compare eight genetic operators in the context of flowshop problems (based on a permutation representation) and conclude that the PMX is the best one for them. 3.4.2. Voting Method The method scans (from left to right) both reference permutations, and uses the rule that each reference permutation votes for its first element that has still not been included in the combined permutation (referred to as the “incipient element”). The voting determines the next element to enter the first still-unassigned position of the combined permutation. This is a min-max rule in the sense that if any element of the reference permutation were chosen other than the incipient element, then it would increase the deviation between the reference and the combined permutations. Similarly, if the incipient element were placed later in the combined permutation than its next available position, this deviation would also increase. So the rule attempts to minimize the maximum deviation of the combined solution from the reference solution under consideration, subject to the fact that the other reference solution is also competing to

contribute. A bias factor that gives more weight to the vote of the reference permutation with higher quality is also implemented for tie breaking. This rule is used when more than one element receives the same number of votes. The element with highest weighted vote is then selected, where the weight of a vote is directly proportional to the objectivefunction value of the corresponding reference solution. Additional details concerning this combination method can be found in Campos et al. (2001).

4. Computational Experiments
In this section we present a number of experiments developed to estimate several registration transformations for two different 3D medical image datasets to study the performance of our proposal. These tests have been carried out under the same conditions since we wanted to extend our conclusions to other possible situations. The most important challenge associated with the current experimentation is that the goal of the IR process is to register two different images of similar objects, instead of two images corresponding to the same object, thus reflecting a more realistic situation in medical IR. On the one hand, we make use of a first dataset from the BrainWeb public repository. It contains four simulated real-world magnetic resonance images (MRIs) of four human brains with noise, anatomical lesions, and a certain degree of occlusion. The main reason for choosing such an image simulator is to ease the comparison of our results with those from other previous or upcoming contributions. On the other hand, this first set of four images will be used as a benchmark to tune the parameters related to the best SS variant. The second one corresponds to a couple of images recently used in Marai et al. (2006), kindly provided by the Rhode Island Hospital. They are computerized tomography (CT) images and correspond to two real human wrists. In this case we want to highlight the complexity of the problem to be tackled as described in Section 4.1. This experimental setup is significantly more complex than those considered in Cordón et al. (2004, 2005). Of course, the registration of different objects is much more complicated than that of different views of the same object, and this motivated the extension of our previous algorithm to obtain good performance in the new IR scenario. The results obtained by the SS algorithm proposed in this contribution for the 3D feature-based IR problem (from now on noted by SS*) will be compared with the old


version of the SS-based IR process (noted simply by SS) (Cordón et al. 2004), and with several IR techniques belonging to the two existing approaches mentioned in Section 1, those searching in the point-matching space and those directly searching in the registration-transformation-parameter space. From the former group, we will consider the recent improvement of the classical ICP algorithm by Liu (2004) (I-ICP), and the hybrid proposal by Luck et al. (2000), combining the ICP algorithm with a simulated-annealing technique in an iterative framework (ICP+SA), with the aim of overcoming the ICP problem of a likely fall in local optima. We should note that, although these two variants of ICP work in the pointmatching space (as does our SS-based proposal), they are based on assigning each transformed scene point to the closest model image point. This way, different scene points can be matched with the same model point, thus resulting in the point matching not being a permutation. On the other hand, an evolutionary approach, the fast real-coded dynamic GA (DynGA) introduced by Chow et al. (2004), will be used from the latter group. Every algorithm maintains its original form and only the fitness function of the GA has been adapted to deal with the uniform scaling factor, not considered in the original proposal (which used only a rigid transformation f, i.e. only rotations and translations were involved in the IR problem). Section 4.1 provides a description of the experimental setup, detailing the 3D images, the IR problems tackled, and the parameter settings. We then present the experiments and the analysis of results in Section 4.2.

4.1. Experimental Setup
This section describes the experimental setup considered to estimate several registration transformations in the two different 3D medical image datasets mentioned above. See the Online Supplement for a description of the 3D datasets used to design the different IR scenarios in our experimentation as well as the parameter settings for the different IR algorithms considered. We introduce here the IR problems considered by describing the pair of images to be registered in each scenario and the four registration transformations applied to each of them. Our results correspond to a number of IR problem instances for the different 3D images considered that have suffered the same four global similarity transformations (noted as T1, T2, T3, and T4 in Table 1) to be estimated by the different 3D IR


algorithms applied. These are ground truth transformations and they will allow us to quantify the accuracy of the IR solution returned by every algorithm. Hence, we will know in advance the optimal (i.e. the exact) registration transformation relating every scene and model input in the two datasets, thus enabling us to compute the objectivefunction value associated with the optimal solution of the problem in the BrainWeb images (see Section 4.2). As mentioned in Section 3.1, similarity transformations involve rotation, translation, and uniform scaling. They can be represented by eight parameters: one for the rotation magnitude (λ), three for the rotation axis (axisx, axisy, axisz), three for the translation vector (tx, ty, tz), and one more for the uniform scaling factor (s). To achieve a good solution, every algorithm must estimate these eight parameters accurately. Values in Table 1 have been selected within the appropriate ranges so that important transformations have to be estimated. Both rotation and translation vectors represent a strong change in the object location. In fact, the lowest rotation angle is 115?. Meanwhile, translation values are also high. Likewise, the scaling factor ranges from 0.8 (in the second transformation) to 1.2 (in the fourth one). In this way, complex IR problem instances are likely to be generated. Table 1: Global Similarity Transformations Considered
λ T1 T2 T3 T4
115.0 168.0 235.0 276.9

-0.863868 0.676716 -0.303046 -0.872872

0.259161 -0.290021 -0.808122 0.436436

0.431934 0.676716 0.505076 -0.218218

-26.0 6.0 16.0 -12.0

15.50 5.50 -5.50 5.50

-4.60 -4.60 -4.60 -24.60

1.0 0.8 1.0 1.2

Moreover, to deal with a set of problem instances with different complexity levels in the human brain MRI dataset (see Table 2), we will consider the following scenarios (from lower to higher difficulty): I1 vs. Ti(I2), I1 vs. Ti(I3), I1 vs. Ti(I4), and I2 vs. Ti(I4). Therefore, every algorithm will face sixteen different IR problem instances in this case, resulting from the combination of the four scenarios and the four different transformations Ti.


Table 2: From Top to Bottom: Increasing Complexity Ranking of the IR Problem Scenarios
IR problem I1 vs. Ti (I2) I1 vs. Ti (I3) I1 vs. Ti (I4) I2 vs. Ti (I4) Scene image Lesion Noise No No No No No No No 1% Model image Lesion Noise No 1% Yes 1% Yes 5% Yes 5%

On the other hand, just four IR problems are defined in the second CT dataset since only two images are available. We will consider the following four IR problem instances: I6 vs. T1(I5), I6 vs. T2(I5), I6 vs. T3(I5), and I6 vs. T4(I5). Therefore, in this case, every algorithm will face four different IR problem instances, resulting from the combination of the I6 vs. I5 scenario and the four different transformations Ti, shown in Table 1.

4.2. Experiments and Analysis of Results
This section reports the results obtained in the experiments. In Section 4.2.1, a preliminary experimental study is made to analyze the performance of the different variants of our SS* proposal, where the two combination operators implemented and several weight vectors in the objective function (see Section 3.2) are tested. Note that the weights in the objective function have been previously normalized as wg=wg,

? merror(σ 0 ) ? wnerror = wnerror ? ? g (σ ) ? ? 0 ? ?
with merror (σ 0 ) and g (σ 0 ) being respectively the matching and registration errors of the initial solution, σ 0 , in order to get a uniform measure of both the matching error (curvature-derived error) and the registration error (g()). Then, the best SS* variant is compared with the previous SS proposal in Cordón et al. 2004 (noted SS) and with the remaining state-of-the-art IR techniques, I-ICP (Liu 2004), ICP+SA (Luck et al. 2000), and Dyn-GA (Chow et al. 2004), to measure the actual performance of our proposal in the problem solving, when considering both the BrainWeb images (Section 4.2.2) and the real-world CT ones (Section 4.2.3).


4.2.1. SS-PMX vs. SS-Voting in the BrainWeb Dataset We have undertaken a detailed comparison of the two combination methods considered, based on the use of the PMX and voting operators, to make a subsequent selection of the best one for inclusion in our final proposal of a SS*-based IR method. For each of the sixteen IR problem instances (specified by a given IR scenario and by one of the four transformations, see Tables 1 and 2), the comparison between both combination methods is made by considering five different values of the coefficients in the F evaluation function. Each of these variants is denoted by WC(x, y), where x and y correspond to the wg and wnerror weighting coefficients in the objective function (see Section 3.2). The weight vectors range from (wg, wnerror) = (0,1), where the search process is guided only by the problem-dependent (image-specific) information (measuring the point-matching quality by the curvature information and not taking into account the registration error g()), to (wg, wnerror) = (1,0), where the search is guided only by the registration error, as is the usual practice. Three intermediate situations are also tested ((wg, wnerror)={(0.2,0.8), (0.5,0.5), (0.8,0.2)}), where a different trade-off is established between both optimization criteria. From this analysis, we can conclude that using the PMX operator in the SS*-based IR method achieves significantly better results than does the voting method. The SS*PMX algorithm obtains both the best minimum and mean results in each of the sixteen IR instances. With regard to the influence of the weight vector values in the behavior of the two SS* algorithm variants, it can be concluded that the weight combination WC(0.,5,

allows us to obtain the best mean value in thirteen of the sixteen cases overall. We will include only a summarized, representative set of results from the broad

experimentation developed. For the complete study, see Santamaria (2006). Figure 2 shows the average results for the minimum and mean values obtained by each weight combination along the sixteen IR problem instances for the PMX combination method. It is easy to observe how the intermediate weight combinations result in the best performance for both indices. Therefore, the worst results are obtained when considering a single term of the objective function, reinforcing our initial intention of making use of additional information (the curvature information in our case) as an additional term to guide the search process in a better way.


Average registration error values of the PMX Combination Method
Average(Min) 2000 1800 1600 Average(Mean)

Registration Error

1400 1200 1000 800 600 400 200 0 WC(0,1) WC(0.2, 0.8) WC(0.5, 0.5) WC(0.8, 0.2) Weights WC(1,0)

Figure 2: Average Registration Error Values of the PMX Combination Method for Different Weighting Coefficients WC. A Good Trade-off Between both Terms of the Objective Function Results in a More Suitable Result. 4.2.2. Comparison Between SS* and Previous Methods in the BrainWeb Dataset In this section we compare our SS* proposal (considering the PMX combination operator and the WC0.5–0.5 weight values) with SS (the previous version of SS*) and with the best state-of-the-art IR algorithms in the literature: I-ICP (Liu 2004), ICP+SA (Luck et al. 2000), and Dyn-GA (Chow et al. 2004). We compare the quality of the solution obtained with these five methods when solving the sixteen instances under consideration. We report the MSE (mean squared error) value of each method on each instance. The MSE value is more adequate for comparing general IR methods than the

g() value described in Section 1, which restricts its application to permutation-based
approaches. It is also a well-known metric in the feature-based IR community (Besl and McKay 1992, Feldmar and Ayache 1996, Yamany et al. 1999, etc.), Moreover, the MSE has recently been proposed as a standard performance measure to prevent unfair comparisons between IR algorithms and motivate statistically accurate analysis (Robinson and Milanfar 2004). The MSE, in which each transformed scene point is assigned to the closest model image point (regardless of whether the latter had been previously assigned to another scene point), is


∑ f (x ) ? y
i i =1


2 i




where yi is the closest model point to the transformed scene point xi. We provide the minimum, maximum, average, and standard deviation of the MSE values on fifteen independent runs. Table 3 reports these values for the BrainWeb dataset experiments obtained by the five IR algorithms. Another advantage of using the BrainWeb dataset is that we know in advance the ground-truth solution of every IR problem instance. This value is shown in brackets together with the name of the transformation, so that we can compare it with the outcomes of every algorithm. Notice that each number in this table is rounded up/down. First, we observe that the MSE values obtained are progressively further from the optimal ones when increasing the complexity of the IR scenario. On the other hand, it can be seen how our SS*-based IR method achieves the best mean performance in fifteen of the sixteen cases, as well as the best minimum MSE value in thirteen of them. Moreover, we should notice that the results obtained by our approach in those instances where it does not achieve the best mean and minimum values of performance can be improved by choosing a different configuration for the w g and the wnerror weights instead of fixing them to a single value for all the instances. However, we preferred to keep them unchanged for all instances to provide a robust method (as we did by selecting the same SS* variant for every case). The poor results of the previous scatter search version, SS, related to the new one, SS*, show that SS does not use that image-derived information properly. At the same time, the low standard deviations show the robustness of SS*. In particular, we achieve one of our goals with respect to our previous work (Cordón et al. 2004), which is to design a competitive IR method even in a complex scenario of inter-subject medical IR. Table 3 is split into four sub-tables considering every IR problem scenario. The best mean and minimum values are shown using the underlined bold font. The results obtained by each method can be compared with the optimal solution (in brackets) for every IR problem instance


Table 3: MSE Values Obtained by the Three State-of-the-Art IR Algorithms, Our Previous SS contribution (Cordón et al. 2004), and Our Current SS* IR method.
T1 [32] DynICP+SA GA 247 101 344 264 307 195 38 51 T3 [32] DynICP+SA GA 457 87 711 678 559 211 81 137 T1 [43] DynICP+SA GA 305 132 432 741 343 299 32 144 T3 [43] DynICP+SA GA 279 139 389 839 347 326 33 174 T1 [46] DynICP+SA GA 236 124 466 1083 385 255 61 228 T3 [46] DynICP+SA GA 312 158 433 468 381 225 43 87 T2 [21] DynICP+SA GA 131 44 131 284 131 108 0 52 T4 [47] DynICP+SA GA 283 139 611 600 465 302 101 121 T2 [30] DynICP+SA GA 237 56 297 534 261 154 18 114 T4 [62] DynICP+SA GA 336 221 429 841 382 354 24 147 T2 [30] DynICP+SA GA 314 48 388 299 359 163 22 58 T4 [67] DynICP+SA GA 342 207 413 1222 367 415 17 258

I-ICP m M 344 -

SS 45 202 142 40



SS 40 155 98 30

SS* 37 50 43 4

? σ

131 35 40 37 2 I1 vs. Ti(I2) SS* 57 67 63 3 I-ICP 632 -

I-ICP m M 894 -

SS 69 192 139 32

SS 85 344 210 58

SS* 49 59 54 3

? σ

I-ICP m M 518 -

SS 132 278 201 45



SS 99 180 133 23

SS* 50 66 57 4

? σ

330 90 132 112 12 I1 vs. Ti(I3)

I-ICP m M 438 -

SS 71 264 188 59

SS* 43 235 64 46

I-ICP 478 -

SS 119 431 315 86

SS* 112 143 123 8

? σ

I-ICP m M 704 -

SS 89 317 231 50



SS 88 179 136 31

SS* 51 167 89 41

? σ

149 1493 269 184 33 I1 vs. Ti(I4)

I-ICP m M 951 -

SS 67 298 207 68

SS* 52 227 82 45

I-ICP 416 -

SS 134 407 301 77

SS* 95 375 154 86

? σ


I-ICP m M 237 -

? σ

T1 [29] DynICP+SA GA 230 108 237 348 236 178 2 60 T3 [29] DynICP+SA GA 399 110 439 611 407 192 10 116

SS 112 347 231 64



128 341 298 193 62 I2 vs. Ti(I4) SS* 70 278 104 67 I-ICP 1588 -

T2 [18] DynICP+SA GA 142 58 341 270 268 106 71 51 T4 [45] DynICP+SA GA 962 164 1533 751 1247 298 209 145

SS 59 209 142 49

SS* 52 188 75 41

I-ICP m M 609 -

SS 96 309 213 67

SS 119 414 256 95

SS* 105 362 150 78

? σ

To show clearly the actual performance of each IR technique when tackling the previously introduced BrainWeb IR problems, Figure 3 collects the graphical representations of the real overlapping achieved by each IR algorithm in four of the sixteen instances considered, one from each IR scenario. The first column of this figure shows the original IR problem instance to be solved by every method, while the next five columns correspond to the best registration estimations achieved by each IR algorithm (from left to right: I-ICP, ICP+SA, Dyn-GA, SS, and SS*). It can be seen how SS* always obtains the best registration (see the little resemblance of the images in the four central columns of the figure with regard to those in the rightmost column). It must be noted that the performance improvement regarding the remaining algorithms is much more remarkable as the IR scenario complexity increases, and that the Dyn-GA approach is the only other IR technique that is able to solve the IR problem properly for the complex scenarios considered in the experimentation developed. The first column in Figure 3 corresponds to the original pose of the brains of four of the sixteen IR instances, from top to bottom: I1 vs T1(I2), I1 vs T2(I3), I1 vs T3(I4) and I2 vs T4(I4). The next five columns correspond to the best registration estimations achieved by each IR algorithm (from left to right: I-ICP, ICP+SA, Dyn-GA, SS, and SS*). 4.2.3. SS* vs. Previous Methods in the CT Human Wrist Dataset Once the performance of our proposal has been validated against the simulated MRI dataset and compared with the IR techniques considered, the final goal of our work regarding the consideration of non-simulated and more complex anatomical medical images will be addressed in this subsection by using the second CT dataset.


In Table 4 and Figure 4 the quantitative and qualitative performance obtained by each of the IR methods considered in the four IR instances (see Section 4.1) are shown. In view of the statistics shown in Table 4, our SS* proposal exhibits more robust behavior according to the MSE mean and standard deviation values. This difference is especially important when comparing the MSE mean value of SS* and the rest of the algorithms. Specifically, the SS* method obtains a mean MSE value of 18, 17, 18, and 19 for T1, T2, T3, and T4 respectively, while ICP_SA obtains 51, 40, 49 and 28, and Dyn_GA obtains 53, 52, 58, and 51. This fact, together with the low standard deviation values, shows that SS* behaves regularly and accurately in the fifteen runs performed. Table 4 is split into four subtables considering every IR problem instance. The best mean and minimum values are shown using the underlined bold font.

Figure 3: Registration of Brain Images.

Nevertheless, the Dyn-GA method achieves the best individual estimation as revealed by the minimum MSE value. This is due to the fact that the objective function of the original proposal of Dyn-GA takes into account the median square error, favoring better performance in severe occluding IR instances, as in the CT dataset. This suggests developing new extensions for the current SS proposal with the intention of achieving

more accurate estimations by considering more robust mechanisms that are able to deal with these kinds of IR instances. In Figure 4, from left to right, the first image corresponds to the original pose of the first IR problem considering T1. The next four images correspond to the best registration estimations achieved by each IR algorithm (IICP, ICP+SA, Dyn-GA, and SS*) Table 4: MSE Values Obtained by the Three State-of-the-Art IR Algorithms and our current SS* IR Method.
T1 T2 DynGA 6 92 53 25 SS

m M 52 -

51 52 51 0 T3

42 -

36 41 40 2 T4

? σ

12 m 49 M 18 ? 9 σ I6 vs. Ti(I5) SS

DynGA 6 87 52 26



12 30 17 6

m M 55 -

45 52 49 2

? σ

DynGA 6 90 58 25

m M 37 -

23 37 28 5

12 49 18 9

? σ

DynGA 6 86 51 24



10 49 19 11

Figure 4: Original Pose and Best Registration Estimations of IR Problem T1.

5. Concluding Remarks
We have described the development and implementation of a metaheuristic procedure for the optimization of IR. Our procedure extends the application of SS in an innovative way with respect to our previous proposal in Cordón et al. (2004) by implementing advanced reference-set designs as well as by strategically including context information derived from the images’ features. This heuristic information is incorporated into an improved solution evaluation, candidate list strategies within the local search method,


and into the diversification-generation method, so that we can deal with significantly more complex problem instances. Indeed, one of the main goals of our effort has been to test the proposed procedure by employing real-world data in realistic scenarios. To make a valid comparison with competing procedures, we have used the well-established MSE metric as well as graphical outputs. Our computational experiments showed that SS* is of merit when compared with IR procedures previously identified as being the best. More specifically, when dealing with the realistic scenarios, we have seen how our SS* IR method achieves the best mean performance in fifteen of the sixteen cases, as well as the best minimum MSE value in thirteen of them. The poor results of the old SS version showed that it did not make a suitable use of image-derived information. On the other hand, when tackling the realworld CT images, our proposal is the most robust method. This outcome can even be improved to obtain more accurate results by means of more suitable mechanisms for this specific class of IR instances (i.e. new objective functions, more complex transformations, hierarchical approaches, etc.). We are planning to tackle the IR problem of 3D range images under the pointmatching approach for the 3D model reconstruction of real-life objects, which is an emerging topic in computer graphics. We also want to extend our work to medical scenarios where real-time registration is a major concern. To do so, we will compare the outcomes of the present matching-space SS* contribution, as well as other previous proposals in the transformation-parameter space (Cordón et al. 2006). These latter approaches could be redesigned to become more competitive in terms of computation time.

Research by Oscar Cordón, Sergio Damas, and Jose Santamaría is supported by the Ministerio de Educación y Ciencia (ref. TIN2006-00829), including EDRF fundings. Research by Rafael Martí is partially supported by the Ministerio de Educación y Ciencia (ref. TIN2006-02696). We would like to acknowledge Professor J.J. Crisco, supported by grant NIH AR44005, for providing us with the CT images.


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