Hindawi Publishing CorporationInternational Journal of Biomedical ImagingVolume 2006, Article ID 73430, Pages 1–9DOI 10.1155/IJBI/2006/73430

Nonrigid Medical Image Registration by Finite-ElementDeformable Sheet-Curve Models

Jianhua Xuan,1 Yue Wang,2 Matthew T. Freedman,3 Tulay Adali,4 and Peter Shields3

1 Department of Electrical Engineering and Computer Science, The Catholic University of America,Washington, DC 20064, USA

2 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University,Arlington, VA 22203, USA

3 Department of Oncology and the Lombardi Comprehensive Cancer Center, Georgetown University,Washington, DC 20057, USA

4 Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County,Baltimore, MD 21250, USA

Received 16 August 2005; Revised 5 May 2006; Accepted 18 May 2006

Recommended for Publication by Ming Jiang

Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor’s early response tochemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are theindicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance(MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports anewly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specif-ically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines isdesigned to model complex local deformations. The experimental results on both digital phantoms and real MR breast imagesusing the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significantand robust improvement in both boundary alignment and local deformation recovery.

Copyright © 2006 Jianhua Xuan et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

1. INTRODUCTION

Chemoprevention is an established approach to cancer pre-vention based on 25 years of research [1–3]. The breastcancer prevention trial (BCPT) in the United States re-cently led the Food and Drug Administration (FDA) to ap-prove tamoxifen as a chemopreventive drug to reduce breastcancer risk [4]. Although the overall benefits of tamox-ifen outweigh the overall risks, the risks were found sig-nificant in the trials, including increased risks of endome-trial cancer, problems related to blood clots, and cataracts[5]. Moreover, many clinical studies have also reported thatnot all women are protected against breast cancer by ta-moxifen. Given the risks and the lack of full protectionagainst breast cancer, some high-risk women are reluctantto use tamoxifen because they want to know whether ta-moxifen is effective on their breasts, rather than relying onstatistical evidence. Hence, it is clinically important to de-

velop novel approaches to accurately assess early response totamoxifen.

Previous studies have shown that estrogens increasebreast density [6, 7], but the effect on breast density ofselective estrogen receptor modulators (SERMs), such astamoxifen and raloxifene, is unknown. In a previous clin-ical trial [6], one author of this paper has evaluated theeffects of two years of treatment with raloxifene, estro-gen, or placebo on breast density using a digitized analy-sis of mammograms. The main findings of the study canbe summarized as the following: (1) the mean breast den-sity was statistically significantly greater in the conjugatedestrogens group than it was in the other three groups,and (2) volunteers receiving raloxifene did not increasebreast density after two years of treatment. In the study,breast density, as measured by mammography, is deter-mined by the relative amounts of fat and fibroglandulartissue present. As discussed in [6], since estrogen action

2 International Journal of Biomedical Imaging

works through estrogen receptors that are present in glan-dular tissue, breast density measured on glandular tissuewould more directly indicate the response to SERM chemo-prevention. However, mammography dose not allow us toaccurately measure the density of glandular tissue in prac-tice.

In this study, we use gadolinium-enhanced MR imagingmethod to differentiate glandular tissue from fibroglandulartissue, and measure the breast density on the glandular tis-sue. In the light of the antiestrogenic properties of tamoxifen,that is, reduction of circulating growth factors and inhibitionof angiogenesis [8], we propose to quantitatively measurechanges of breast density and breast vascular density in glan-dular tissue to assess early response to tamoxifen. With suchquantitative measurements, we can determine whether thedrug is effective for a patient, early and individually. To com-pare the glandular tissue volumes between pre- and post-contrast MR images, nonrigid registration should be per-formed to align two MR images and recover local deforma-tions.

Many nonrigid registration methods have been proposedin recent years [9–11], and can be categorized into intensity-based or feature-based approaches [12–15]. When usinggadolinium-enhanced MR images, feature-based approachesoffer advantages over intensity-based registration methodssince geometric features—points, curves, and surfaces—areconsistent between pre- and postcontrast MR images whileintensity values are not. By establishing feature correspon-dences, deformations can be recovered by nonlinear interpo-lation methods such as the thin-plate-spline (TPS) method[13].

Registration accuracy often depends on the availabil-ity of a large number of features. Point-based registrationmethods typically use anatomical landmark points as thefeatures to register images [13]. Although anatomical land-marks are the most robust features, the number of land-marks is usually limited in MR breast images. Moreover,if local deformations are confined in certain regions, thelandmarks have to be well placed over those regions. Itis therefore reasonable to use the boundaries of anatom-ical objects as features to recover local deformations [14,16].

Several approaches have been proposed that used bound-aries or curves as matching features [14, 16–18]. Mosh-feghi proposed an elastic matching method for registra-tion of multimodality images [14]. The method is based onBurr’s elastic contour matching algorithm and a Gaussiansmoothed deformation model that estimates the deforma-tions over the whole image [19, 20]. To reduce the com-putational complexity, Davatzikos et al. proposed a two-step approach to recover deformations based on bound-ary mapping, that is, a homothetic mapping of boundariesfollowed by an elastic deformation transformation (EDT)[16].

However, elastic matching method often suffers from thefollowing problems: (1) since no physical model (or con-straint) is imposed on the curve deformation, the resultedcurve matching is considered only an ad hoc solution; and

(2) the Gaussian smoothed deformation model is an over-simplified model when facing the complex local deforma-tions existed in MR breast images. For example, althoughDavatzikos’ method is a very efficient algorithm resulting ina great reduction in computational complexity, the key as-sumption behind is homothetic mapping, that is, a map-ping by a uniform scaling of its length and arbitrary length-preserving bending [16]. Such an assumption cannot holdin MR breast images in that the participated deformationsare relatively large, therefore homothetic mapping could pro-duce large curve matching error.

In this paper, we propose deformable sheet-curve modelsto overcome the problems associated with the existing meth-ods. Deformable curves are used to obtain a reliable match-ing of the curves using physically constrained deformations,and a deformable sheet with the energy functional of thin-plate splines is used to model complex local deformationsbetween the images.

2. THEORY AND METHOD

In image registration a nonlinear transformation U(x) =(Ux(x, y),Uy(x, y)) is used to map the first image to the sec-ond image. We refer to the function U(x) as the deforma-tion, which can be modeled as either thin-plate splines [13]or elastic sheets [16]. Under the assumption that we haveK pairs of corresponding boundaries (or curves), (C1i,C2i),i = 0, . . . ,K − 1, we use deformable curves to model thedeformation of curves in the two images (denoted as Uc),and a deformable sheet to model the deformation betweentwo images (denoted as Us). We propose a deformable sheet-curve model shown in Figure 1 to recover the deformationUs through matching the deformable curves and deformingthe deformable sheet iteratively.

A deformable sheet-curve model can be presented invariational and finite-element forms. We denote the defor-mation of the sheet by Us(x, y, t) = (Ux

s (x, y, t),Uys (x, y, t)),

where (x, y) is the bivariate material coordinate and t is thetime index. Similarly, the deformation of the curve can bedenoted by Uc(s, t) = (Ux

s (s, t),Uys (s, t)), where s is the uni-

variate material coordinate of the curve. The strain energy Ecan be found to characterize the deformable material of thesheet or the curve as an instance of the spline function. Atthe center of our method, the continuum mechanical equa-tion [21]

μ∂2U∂t2

+ γ∂U∂t

+δE(U)δU

= f(U) (1)

governs the nonrigid motion of the sheet (curve) in responseto the extrinsic force f(U), where μ is the mass density func-tion of the deformable sheet (curve) and γ is the viscosityfunction of the ambient medium. The third term on the left-hand side of the equation is the variational derivative of thestrain energy functional E, the internal elastic force of thesheet (curve).

Jianhua Xuan et al. 3

Deformable sheet

C11C13

C14

C15C16

C12

Deformable sheet

Deformablecurves

C21

C23

C24

C25C26

C22

Deformablecurves

Figure 1: Configuration of the deformable sheet-curve model.

2.1. Variational modeling

The deformable energy for the deformation Us(x, y, t) of thesheet is defined by

Es(x, y) =∫∫ ⎛⎝w10

∣∣∣∣∣∂Us

∂x

∣∣∣∣∣2

+ 2w11

∣∣∣∣∣∂Us

∂x

∣∣∣∣∣∣∣∣∣∣∂Us

∂y

∣∣∣∣∣

+ w01

∣∣∣∣∣∂Us

∂y

∣∣∣∣∣2

+ w20

∣∣∣∣∣∂2Us

∂x2

∣∣∣∣∣2

+2w22

∣∣∣∣∣∂2Us

∂x∂y

∣∣∣∣∣2

+ w02

∣∣∣∣∣∂2Us

∂y2

∣∣∣∣∣2⎞⎠dx dy,

(2)

where the weights w01, w11, and w10 control the tensions ofthe sheet (stretching energy), and w02, w22, and w20 controlits “rigidities” by weighting the bending energy (see [22] fora detailed discussion on maximal rigidity). Accordingly, thedeformable energy for the deformation Uc(s, t) of the curveis given by

Ec(x, y) =∫∫ ⎛⎝w1

∣∣∣∣∣∂Uc

∂s

∣∣∣∣∣2

+ w2

∣∣∣∣∣∂2Uc

∂s2

∣∣∣∣∣2⎞⎠ds. (3)

The weight w1 controls the tension along the curve (stretch-ing energy), and w2 controls its rigidity (bending energy).

The dynamics of the deformable sheet-curve model is de-fined by the following Lagrangian motion equations:

μ∂2Ui

c

∂t2+ γ

∂Uic

∂t+δE(

Uic

)δUi

c= f ic, i = 0, . . . ,K − 1,

μ∂2Us

∂t2+ γ

∂Us

∂t+δE(

Us)

δUs= fs,

(4)

where fs and f ic are the external forces applied on the sheetand the curve, respectively. The registration process is an

iterative procedure by repeating the following two steps.

(1) Boundary deformation recovery by deformable curvematching: deformable curves use the correspondingcurves in the second image to define the external forces(f ic , i = 0, . . . ,K − 1). The deformations of the curves(Ui

c(s, t), i = 0, . . . ,K − 1) can then be obtained bysolving Lagrangian motion equations of the curves.

(2) Image deformation recovery by deforming the deforma-ble sheet: the deformable sheet uses the new curve po-sitions (Cnew

1i = C1i + Uic, i = 0, . . . ,K − 1) to define

the external forces (fs). The local deformation of theimage can be obtained by solving Lagrangian motionequation of the sheet.

The final deformation Us is obtained when the energies ofthe deformable sheet-curve model reach their minima.

We define the external forces f ic to reflect the mismatchbetween the two corresponding curves (C1i,C2i) : f ic = D(C1i,C2i), where D(C1i,C2i) is the Gaussian weighted Euclideandistance of each point on the first curve to the nearest pointon the second curve. After we recover the curve deformationsUi

c, we obtain the new curve positions as Cnew1i = C1i + Ui

c, i =0, . . . ,K − 1. Then we use Gaussian weighted Euclidean dis-tance to define the external forces for the deformable sheet,that is, fs = D((C1i,Cnew

1i ), i = 0, . . . ,K − 1).

2.2. Finite element modeling

We use a finite-element method to compute the numericalsolutions of Us and Uc. We first tessellate the continuous ma-terial domain, (x, y) for the sheet and s for the curve, intoa mesh of m element subdomains Dj . We then approximateU by a weighted sum of continuous basis functions Ni (so-called shape functions): U ≈ Uh = ∑n

i=1 UiNi = Na, whereUi is a vector of nodal variables associated with mesh nodei, Ni is a vector of shape functions associated with node i, nis the number of nodes of an element, aT = [UT

1 , . . . , UTn ],

and N = [N1, . . . , Nn]. The shape functions Ni are fixed inadvance and the nodal variables Ui are the unknowns. The

4 International Journal of Biomedical Imaging

motion equation (4) can then be discretized as

M∂2a∂t2

+ C∂a∂t

+ Ka = F, (5)

where M is the mass matrix, C is the damping matrix, K isthe stiffness matrix, and F is the forcing matrix. M, C, and Fcan be obtained as follows:

M =∫∫

μNTNdx dy,

C =∫∫

γNTNdx dy,

F =∫∫

NT f dx dy.

(6)

To compute K, we have the following equation:

K =∫∫ (

NTs αNs + NT

b βNb)dx dy, (7)

where

Ns =[∂N∂x

,∂N∂y

]T

, Nb =[∂2N∂x2

,∂2N∂x∂y

,N∂y2

]T

,

α =[w10 w11

w11 w01

], β =

⎡⎢⎢⎣w20 0 0

0 w22 0

0 0 w02

⎤⎥⎥⎦ .

(8)

2.2.1. Deformable sheet element

The deformable sheet consists of a set of connected triangularelements. Barycentric coordinates are the natural choice fordefining shape functions over a triangular domain, since aunifying representation of different triangles can be achieved[23]. Barycentric coordinates (L1,L2,L3) are defined by thefollowing mapping with material coordinate (x, y):

⎡⎢⎢⎣x

y

1

⎤⎥⎥⎦ =

⎡⎢⎢⎣x1 x2 x3

y1 y2 y3

1 1 1

⎤⎥⎥⎦

⎡⎢⎢⎣L1

L2

L3

⎤⎥⎥⎦ , (9)

where (x1, y1), (x2, y2), and (x3, y3) are the coordinates ofthree vertex locations of a triangle.

We construct a 9-degree-of-freedom (DOF) triangularelement with its position and first parametric partial deriva-tives at each triangle vertex. The shape functions for the 9-DOF triangle are [24]

N9T1 =

⎡⎢⎢⎣

L1 + L21L2 + L2

1L3 − L1L22 − L1L

23

c3(L2

1L2 + 0.5L1L2L3)− c2

(L2

1L3 + 0.5L1L2L3)

−b3(L2

1L2 + 0.5L1L2L3)

+ b2(L2

1L3 + 0.5L1L2L3)

⎤⎥⎥⎦ .

(10)

The triangle’s symmetry in barycentric coordinates can beused to generate the shape function for the second and thirdnodes in terms of the first. To generate N9

2 use the aboveequations but add a 1 to each index so that 1→2, 2→3, and

3→1. The N93 functions are generated by adding another 1

to each index. By defining 9 DOFs of the triangular elementas aT = [U1, U1x, U1y , U2, U2x, U2y , U3, U3x, U3y], we can ap-proximate the Us as

Uhs = N9a =

[N9

1 N92 N9

3

]a. (11)

2.2.2. Deformable curve element

The finite element of the curve has 4 DOFs for the two nodeslocated atthe ends of the curve segment. The DOFs at eachnode are the position and tangent of the node. The defor-mation of the curve segment can be approximated as theweighted sum of a set of Hermite polynomials [23]:

Uc ≈ Uhc (s) =

3∑i=0

UicNi, (12)

where Uic, i = 0, . . . , 3, are nodal variables and Ni, i = 0, . . . , 3,

are given as follows:

N0 = 1− 3(s

h

)2

+ 2(s

h

)3

,

N1 = h

(s

h− 2

(s

h

)2

+(s

h

)3)

,

N2 = 3(s

h

)2

− 2(s

h

)3

,

N3 = h

(−(s

h

)2

+(s

h

)3)

,

(13)

where h is the parametric element length.

2.2.3. Numerical integration

The deformable sheet-curve model can be stabilized duringthe matching process if its motion is critically damped tominimize vibrations. Critical damping can be achieved byappropriately balancing the mass and damping distributions.A simple way of eliminating vibration while preserving use-ful dynamics is to set the mass density in (1) to zero, thusreducing (5) to

C∂a∂t

+ Ka = F. (14)

This first-order dynamic system governs the model thathas no inertia and comes to rest as soon as all the forcesbalance. We integrate (14) using an explicit first-order Eu-ler method [24]. The method begins with a simple forwarddifference approximation. By considering extrapolation fromtime with the forward difference approximation

∂a∂t= a(t + Δt)− a(t)

Δt, (15)

(14) then becomes

Ca(t + Δt) = (C− ΔtK)a(t) + ΔtF. (16)

Jianhua Xuan et al. 5

Thus we obtain the updating formula as follows:

a(t + Δt) = (I− ΔtC−1K)

a(t) + ΔtC−1F. (17)

It is well known that finite difference methods for initial-value systems yield expressions very similar to the above re-sults obtained by finite-element schemes [25]. A noteworthydistinction is that the coefficient matrix for finite differenc-ing is diagonal in the usual difference approximation. Thisleads, in the forward difference approximation, to an effi-cient algorithm for solving the problem. In the finite-elementmethod, due to the sparseness of C, it is difficult to com-pute C−1 since complicated singular decomposition meth-ods have to be used. Fortunately, a physical solution existsin computational mechanics, namely, the “lumping” proce-dure, to overcome such difficulties. The idea can be physicallyinterpreted as replacing the continuous material with the dis-tributed mass, that is, concentrated material with “lumped”mass (“beads”) at the nodes. In practice, there are severalways to perform such a “lumping” procedure—for example,using modified shape functions or different numerical inte-gral methods [24]. Among those, the easiest way is to keeponly the diagonal coefficients and is adopted here in solvingthe motion equations of the deformable sheet-curve model.

3. EXPERIMENTAL RESULTS

Deformable sheet-curve models were implemented using afinite-element method using 9-DOF triangular sheet ele-ments and 4-DOF curve elements. The deformable curvesare used to match boundaries, and the deformable sheet ismodeled as a thin-plate spline to recover the local deforma-tion. The registration process is an iterative procedure by re-peating the following two steps: (1) the deformations of thecurves (Ui

c) are obtained by solving Lagrangian motion equa-tions of the curves with the external forces (f ic) defined by thecorresponding curves, and (2) the deformation of the sheet(Us) is obtained by solving Lagrangian motion equation ofthe deformable sheet with the external forces (fs) defined bythe new curve positions (derived from Ui

c). The final defor-mation of the whole image is recovered when the energiesof the deformable sheet-curve model reach their minima.In our experiments, we applied the deformable sheet-curvemodels to both simulated data and real breast MR images todemonstrate the performance of the method in recoveringboundary deformation and image deformation.

3.1. Simulation result

Digital phantoms are generated and used to validate theproposed boundary-based registration approach in a con-trolled manner with known truth. As shown in Figure 2, theboundaries in the first image are depicted in solid line whilethose in the second image are in dashed line. In our ap-proach, we use deformable curves to model the boundariesand match them by recovering the deformation iteratively.The recovered boundary deformation demonstrates an excel-lent matching between the initial and target boundaries witha mean square error (MSE) less than 2%, where the parame-

(a) (b)

(c) (d)

Figure 2: A simulation result deformation recovered by the de-formable sheet-curve model: (a) curve features, (b) recoveredboundary deformation, (c) recovered image deformation, and (d)recovered image deformation in mesh representation.

ters (i.e., the weights in (3)) are experimentally chosen to ob-tain a good overall matching performance. The deformablesheet works cooperatively with deformable curves to infer theimage deformation iteratively. As we can see from Figure 2,the recovered image deformation is constrained by the thin-plate-spline functional. The digital phantom study thereforeprovides compelling evidence that deformable sheet-curvemodels can achieve accurate boundary matching and com-plex image deformation modeling.

3.2. Registration of breast MR images

We have further applied our deformable sheet-curve modelto register real contrast-enhanced sequential MR breast im-ages to assess early response to chemoprevention. The dataset of this study consists of serial studies in 29 women athigh risk of breast cancer who received either tamoxifenor no drug. MR images are acquired with contrast agent(gadolinium) at 0, 3, and 6 months along with biopsies forhistopathology, RNA, and gene analysis at Lombardi CancerCenter (LCC) of the Georgetown University Medical Center.As of October of 2004, we have completed baseline testingon 26 enrolled women, three MR scans on 26 women andbiopsies on 22 women.

MR images are acquired using Siemens’ Magnetom Vi-sion with magnetic field strength of 1.5 Tesla. One 3D MRscan of the patient results in 64 images with pixel resolutionat 512× 512, where pixel spacing is 0.36 mm in both X- andY-directions, and slice thickness is 2.0 mm in Z-direction.Some example images are shown in Figure 3, where pre- and

6 International Journal of Biomedical Imaging

(a)

(b)

Figure 3: Sample MR pre- and postcontrast images at (a) 0 monthand (b) 3 months: (left) precontrast image and (right) postcontrastimage.

(a) (b)

Figure 4: A pair of MR breast images: (a) precontrast image, and(b) postcontrast image.

postcontrast images at 0 and 3 months are shown in Figures3(a) and 3(b), respectively.

We use fibroglandular tissue as the reference object toregister pre- and postcontrast MR breast images, see Figure 4for an example. The regions of fibroglandular tissue are ex-tracted using a stochastic model-based segmentation methodbased on the standard finite normal model (SFNM) and acontextual Bayesian relaxation labeling (CBRL) procedure[3]. The segmentation result is shown in Figure 5 with allthe regions of fibroglandular tissue identified in both pre-and postcontrast images. Then all the corner points onthe boundaries of fibroglandular tissue are extracted (seeFigure 5). By establishing the corner point correspondence,we can partition the boundaries of fibroglandular tissue intocorresponding curves in pre- and postcontrast images. These

(a) (b)

Figure 5: Extracted fibroglandular tissue with corner pointed de-tected: (a) precontrast image, and (b) postcontrast image.

curves serve as the features in the registration of pre- andpostcontrast MR breast images.

The deformable curves with 4-DOF finite elements areused to model the extracted boundaries of fibroglandulartissue, and the deformable sheet with 9-DOF finite elementsis then used to model image deformation. The registrationprocess is a cooperative procedure by repeating boundarymatching and image deformation inferring. The deformablecurve matching gives us the boundary deformation, and therecovered boundary deformation is then used to deformthe deformable sheet to derive image deformation. In ourfinite-element implementation, we use numerical integra-tion method described in Section 2 to solve the Lagrangianmotion equations. The final deformation of the image isrecovered when the energies of the deformable sheet-curvemodel reach their minima. Figure 6 shows the extracted re-gions of glandular tissue by subtracting the registered pre-contrast image from the postcontrast image.

As a comparison, we show the extracted regions of glan-dular tissue using the registration result of the point-basedTPS method in Figure 7. As we can see, many false regionsof glandular tissue are resulted from the misalignment of theboundaries of fibroglandular tissue due to the limited num-ber of the available feature points (25 feature points in thisexperiment). The boundary misalignment can be clearly seenin Figure 7(b), where two regions of fibroglandular tissue areregistered by the point-based TPS method. Figure 7(a) showsthe boundary registration result using our deformable sheet-curve model, which demonstrates a great improvement inboundary alignment compared with that of the point-basedTPS method. The experimental results show that our de-formable sheet-curve model can provide an expected im-provement in registration accuracy regarding both boundaryalignment and local deformation recovery.

4. CONCLUSION

We have developed deformable sheet-curve models for non-rigid medical image registration. The registration methodsupported by our deformable sheet-curve models is afeature-based approach in that (1) deformable curves areused to model curve features—the boundaries of objects,

Jianhua Xuan et al. 7

(a) (b)

Figure 6: Extracted glandular tissue: (a) using the deformablesheet-curve model, and (b) using TPS with feature points alone.

(a) (b)

Figure 7: Registered boundaries of two selected regions: (a) usingthe deformable sheet-curve model, and (b) using TPS with featurepoints alone.

and (2) the thin-plate-spline sheet is used to model thelocal deformation over the image. The deformable sheet-curve model is also a dynamic system that is governed byLagrangian motion equations. The model has been imple-mented in finite-element method with 9-DOF triangularsheet elements and 4-DOF curve elements. By solving La-grangian motion equations iteratively, we can recover the de-formation over the whole image. We have applied the de-formable sheet-curve model to register MR breast imagesfor the assessment of early response to chemoprevention.The experimental results have demonstrated a significant im-provement in the registration accuracy with reliable bound-ary alignment and accurate local deformation recovery. Webelieve that our comparative studies provide useful informa-tion on the utility of the proposed method for nonrigid im-age registration. Given the difficulty of the task, while the op-timality of the method may be data or modality dependent,we would expect it to be an important tool in change detec-tion across temporal image sequences.

ACKNOWLEDGMENT

This work was supported in part by the Department of De-fense under Grant DAMD17-03-0448.

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Jianhua Xuan received his Ph.D. degree inelectrical engineering and computer sciencefrom the University of Maryland in 1997.He received his B.S., M.S., and Ph.D. degreesfrom University of Zhejiang, China, in 1985,1988, and 1991, respectively, all in electri-cal engineering. Currently, he is an Assis-tant Professor in the Department of Elec-trical Engineering and Computer Science atThe Catholic University of America. His re-search interests include biomedical image analysis, cellular andmolecular imaging, computational bioinformatics, systems biol-ogy, intelligent information systems, visual intelligence, computervision, information visualization, and machine learning. He is arecipient of several NIH/DoD/NASA grant awards, and leads agroups effort in cancer research and biomedical research. He is alsoa Member of the Editorial Board for BioMedical Engineering On-Line.

Yue Wang received his B.S. and M.S. degreesin electrical and computer engineering fromShanghai Jiao Tong University in 1984 and1987, respectively. He received his Ph.D. de-gree in electrical engineering from Univer-sity of Maryland Graduate School in 1995.In 1996, he was a Postdoctoral Fellow atGeorgetown University School of Medicine.From 1996 to 2003, he was an Assistantand later Associate Professor of electricalengineering at The Catholic University of America, Washington,DC. Since 2003, he has been an Associate Professor of electri-cal, computer, and biomedical engineering at Virginia Polytechnic

Institute and State University, Arlington, Va. He is also an affil-iated faculty member of radiology and radiological science withthe Johns Hopkins University. He became a Fellow of The Amer-ican Institute for Medical and Biological Engineering (AIMBE) in2004. His research interests focus on intelligent computing, ma-chine learning, pattern recognition, statistical visualization, andadvanced imaging and image analysis, with applications to molec-ular analysis of human diseases.

Matthew T. Freedman is a Radiologistand an Associate Professor of oncology,Lombardi Comprehensive Cancer Center,Georgetown University Medical Center,Washington, DC. He is Director of the Ad-vanced Cancer Imaging Division of the ISISImaging Science Research Center at George-town. He is, at Georgetown, the lead Ra-diologist for two national cancer institutestudies of the effectiveness of screening forlung cancer. He does research in the imaging of rodent mammaryglands studying the effect of dietary changes on mammary glanddevelopment and early carcinogenesis. He studies problems in thedetection of lung cancer and the effect of computer aided detec-tion (CAD) of lung cancer on radiologist performance. He pro-vides medical guidance to graduate students in the biomedical en-gineering/computer science programs at The Catholic University ofAmerica and the Advanced Research Institute of Virginia Tech.

Tulay Adali received the Ph.D. degree inelectrical engineering from North CarolinaState University, Raleigh, in 1992 and joinedthe faculty at the University of Maryland,Baltimore County (UMBC), Baltimore, thesame year. She is currently a Professor in theDepartment of Computer Science and Elec-trical Engineering at UMBC. She worked inthe organization of a number of interna-tional conferences and workshops includ-ing the IEEE International Conference on Acoustics, Speech, andSignal Processing (ICASSP), the IEEE International Workshop onNeural Networks for Signal Processing (NNSP), and the IEEE In-ternational Workshops on Machine Learning for Signal Process-ing (MLSP). She was the general cochair for the NNSP workshops2001–2003 and the technical chair of the MLSP workshops 2004–2006. She is the past chair and current Member of the MLSP Tech-nical Committee, and is serving on the IEEE publications boardand the IEEE Signal Processing Society conference board. Her re-search interests are in the areas of statistical signal processing,machine learning for signal processing, biomedical data analysis(functional MRI, MRI, PET, CR, ECG, and EEG), bioinformatics,and signal processing for optical communications. She is the recip-ient of a 1997 National Science Foundation CAREER Award.

Peter Shields is currently a Full Professor inthe Departments of Oncology and Medicineat Georgetown University. He is the Asso-ciate Director for Cancer Control and Pop-ulation Sciences in the Lombardi Compre-hensive Cancer Center, and the ProgramLeader for Cancer Genetics and Epidemiol-ogy. Also, he is the Director of the Divisionof Cancer Genetics and Epidemiology in theDepartment of Oncology. Dr. Shields’ focusof research is on gene-environment interactions for cancer risk.

Jianhua Xuan et al. 9

The laboratory component of his research develops new biomark-ers of cancer risk, while his epidemiological component accruesspecial populations. A major initiative that is led by Dr. Shieldsis a Breast Cancer Center of Excellence. This program is a multi-investigator and multidisciplinary approach to understand why al-cohol drinking causes breast cancer. Dr. Shields has over 120 peer-reviewed publications, included in high profile journals. He serves,or has served, on editorial boards of major journals such as Car-cinogenesis and Cancer, Epidemiology, Biomarkers and Preven-tion. At Georgetown University, he is a Member of the LombardiComprehensive Cancer Center Executive Committee, the oversightcommittee for the Tumor Biology program, and the M.D.-Ph.D.program.