2102 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 7, JULY 2009

Carotid Artery Motion Estimation From Sequencesof B-Mode Ultrasound Images: Effect of Scanner

Settings and Image NormalizationSpyretta Golemati, Member, IEEE, John S. Stoitsis, Student Member, IEEE, Dimitrios A. Perakis, Emily Varela,

Anastasia Alexandridi, Constantinos H. Davos, and Konstantina S. Nikita, Senior Member, IEEE

Abstract—The motion of the carotid artery wall can quantita-tively be estimated from sequences of B-mode ultrasound images.In this paper, the effects of dynamic range (DR) and persistence,along with that of image normalization, were studied, in anattempt to suggest optimal values for reliable motion analysis.Image sequences were recorded using four different values forDR, i.e., 0, 48, 66, and 90 dB, and three different values forpersistence, i.e., 0, 5.6, and 50. Radial and axial displacements,as well as the correlation coefficients (CCs), were estimated usingblock matching from recordings with durations of about 3 s. Thevariances of radial and axial displacements were not significantlyaffected by changes in DR and persistence. The mean value ofthe CC, which is an index of the reliability of motion analysis,was also not significantly affected by these settings. However, anincrease in persistence increased the delays between peak radialdisplacements and cardiac systole. Image normalization did notaffect the results of motion analysis. It is suggested that high valuesof DR (66 or 90 dB) and low values of persistence (0 or 5.6) are usedfor motion analysis based on block matching.

Index Terms—Block matching, carotid artery, dynamic range(DR), motion analysis, normalization, persistence, ultrasoundimaging.

I. INTRODUCTION

U LTRASOUND imaging of the carotid artery is widelyused in the diagnosis of atherosclerosis, because it allows

noninvasive assessment of the degrees of stenosis and plaquemorphology [4]. Analysis of the digitized ultrasound imagesof the carotid atheromatous plaque provides quantitative mea-sures of plaque texture, which can be used to identify plaquetype (e.g., symptomatic or asymptomatic) [2]. In addition tothis, temporal sequences of ultrasound images can be used toestimate the movement of the carotid artery wall. Within thesesequences, motion can be estimated by tracking the specklepatterns generated by the tissue [5].

Manuscript received November 24, 2007; revised June 1, 2008. First pub-lished May 5, 2009; current version published June 10, 2009. The AssociateEditor coordinating the review process for this paper was Dr. George Giakos.

S. Golemati is with the National and Kapodistrian University of Athens,10561 Athens, Greece (e-mail: sgolemati@biosim.ece.ntua.gr).

J. S. Stoitsis, D. A. Perakis, and K. S. Nikita are with the School ofElectrical and Computer Engineering, National Technical University of Athens,15773 Athens, Greece (e-mail: knikita@cc.ece.ntua.gr).

E. Varela, A. Alexandridi, and C. H. Davos are with the Biomedical ResearchFoundation, Academy of Athens, 11527 Athens, Greece.

Digital Object Identifier 10.1109/TIM.2009.2015536

The motion of the arterial wall is caused by stresses due toblood pressure, blood flow, and tethering to the surroundingtissue. Stresses can result in strains in three directions, which, inrelation to a longitudinal B-mode ultrasound image, correspondto changes in the radius of the vessel, changes in axial length,and movement out of the B-mode section.

Tracking carotid artery wall motion in real time from a seriesof ultrasound images is challenging due to the fact that theimages are noisy and rapidly acquired (≈40 ms), motion isnonrigid, and image contrast/intensity may change over timedue to the flowing blood and/or contrast agent. Previous workon motion analysis of the carotid artery wall and plaque fromsequences of images includes the estimation of tissue deforma-tion during the cardiac cycle using block matching and opticalflow techniques [1], [5], [8].

An important issue affecting reliable recording of sequencesof images is related to the values of the ultrasound scannersettings. Scanner settings may affect the appearance of anatom-ical structures and, therefore, the interpretation of the resultingimages. As a consequence of this, image-processing techniquesmay also be affected by machine settings, and this has an im-plication on the interpretation of the image-processing results.Image normalization has been used in ultrasound images ofcarotid artery and has been shown to affect texture analysisresults [3].

Two of the scanner settings that are likely to affect theestimation of tissue motion from ultrasound images are dy-namic range (DR) and persistence [6]. DR is related to thenumber of different gray levels that appear within the image [7].DR can be defined as the ratio of the highest signal intensitythat can be recorded by the ultrasound device to the lowestsignal intensity, and the scale is logarithmic. The highest signalintensity corresponds to the brightest regions of the real image,whereas the lowest signal intensity corresponds to the darkestregions. After the signal is converted to digital form, high valuesof DR result in a large number of discrete gray levels of theimage, whereas small values of DR correspond to relativelyfew discrete gray levels. An alternative and commonly usedname for persistence is frame averaging. The pixel values fromconsecutive frames are averaged, usually following a weightingregime in which, for example, the average intensity value is acombination of the pixel value from the current frame, one halfof the value from the previous frame, and one quarter of thevalue from the frame before that. If the persistence control is

0018-9456/$25.00 © 2009 IEEE

GOLEMATI et al.: CAROTID ARTERY MOTION ESTIMATION FROM SEQUENCES OF B-MODE ULTRASOUND IMAGES 2103

Fig. 1. (a) Examples of image ROIs interrogated and corresponding histograms. (b) Histogram for ROI on the anterior wall–lumen interface. (c) Histogram forROI on the posterior wall–lumen interface. (d) Histogram for ROI on the posterior wall adventitia–tissue interface.

set to zero, then there is no averaging performed. Increase in thepersistence value on the machine will, in general, increase theweighting factor and the number of previous frames included inthe averaging process.

The purpose of this paper is to study the effect of two scannersettings, i.e., DR and persistence, as well as the effect of imagenormalization on the results of motion analysis of the carotidartery wall from sequences of B-mode ultrasound images. Thefindings are expected to have implications on the definitionof the optimal values of these settings for reliable motionanalysis.

II. METHODS

A. Subjects and Procedures for Data Acquisition

A total of three sets of six image regions of interest (ROIs)of healthy subjects were investigated. The image ROIs in thefirst set were situated on the posterior wall–lumen interface,those of the second set were situated on the anterior wall–lumeninterface, and those of the third set were situated at the interfaceof the adventitial (outermost) layer of the posterior wall with thesurrounding tissue. In each image, selected ROIs were alignedalong the radial direction (Fig. 1). The ROIs of each set werecharacterized by different intensity patterns, as can be seenfrom the examples of the histograms of Fig. 1. The interrogationof image ROIs with different intensity patterns was undertakenin an attempt to cover a somewhat wider range of imagingscenarios.

All investigations were performed with a Vivid 7(General Electric) ultrasound scanner with a high-resolution7.5-MHz linear array scanhead. Each carotid artery was imagedin the longitudinal section to enable the estimation of bothradial and axial motion. Focus was adjusted for each subjectto achieve maximal wall surface definition. The images werenormally magnified to a resolution of 10 pixel/mm. Thiscalibration factor was equal in both directions. The frame ratewas 25 Hz.

The subjects were examined supine with a slight backwardtilt of the head. To minimize movement caused by factorsother than hemodynamic forces, the operator held the probewith both hands, and the subjects held their breath for afew seconds during the recording procedure. The probe waslightly placed on the subject’s neck to enable maximal stabi-lization of the probe but minimal deformation of the underlyingtissue.

The sonographer selected a B-mode slice across the widestpart of the vessel with optimal reflections from the near andfar walls. The vertical distance between the two walls was thenassumed to represent the vessel diameter. The motion of thewall toward and away from the probe was assumed to be radialmotion [Fig. 1(a)]. Motion perpendicular to the ultrasoundbeam was assumed to be axial motion. It was recognized thatthe displacement of the whole vessel within and out of the im-age plane may sometimes occur during recording, introducingdifficulties in interpreting wall motion.

The measurements were made at a constant-temperatureroom (26 ◦C).

2104 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 7, JULY 2009

Fig. 2. Examples of B-mode ultrasound images of the carotid artery obtained with different values of DR and corresponding histograms. (a) and (b) DR = 0 dB.(c) and (d) DR = 48 dB. (e) and (f) DR = 66 dB. (g) and (h) DR = 90 dB. The boxes in (a), (c), (e), and (g) indicate the ROIs for which the histogram wasestimated.

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Fig. 3. Examples of B-mode ultrasound images of the carotid artery obtained with different values of persistence and corresponding histograms and frequencyspectra. (a)–(c) Persistence = 0. (d)–(f) Persistence = 5.6. (g)–(i) Persistence = 50. The boxes in (a), (d), and (g) indicate the ROIs for which the histogram andspectrum were estimated.

The images were stored in Digital Imaging and Communi-cations in Medicine (DICOM) format. The DICOM standard iswidely used to store, manage, and transfer medical data, mainlybecause it allows connectivity, compatibility, and optimizationof the flow of data.

One image sequence was recorded using reference valuesfor DR (66 dB) and persistence (0). Two additional sets of se-quences were recorded. The first set included three sequences,in which the DR was set to 0, 48, and 90 dB, respectively; thepersistence was set to 0 for these three sequences. The secondset included two sequences, in which persistence was set to5.6 and 50, respectively; the DR was set to 66 dB for thesetwo sequences. All other scanner settings were constant for allsequences. As a first approximation, the most often used valuesin a clinical setting were included in the study, i.e., 48 or 66 dBfor DR and 5.6 for persistence. Along with these, two extreme(minimal and maximal) values seemed a reasonable choice forthe effect of these settings on motion analysis. In particular,

a persistence value that is equal to zero, which was used asa reference value, was an obvious choice for motion analysis.It allows for maximal temporal resolution, because no frameaveraging is performed.

Figs. 2 and 3 show examples of images obtained usingdifferent values of DR and persistence, respectively. The his-tograms of an ROI on the posterior wall are also shown in thefigures. As we can see, as the DR increases, pixel intensitiesare accommodated in a narrower range (Fig. 2). What may notbecome readily obvious from these histograms is that moreintensity bins are available in the narrow-range cases; that is,intensity resolution increases as the DR increases. IncreasedDR also seems to increase image contrast. Specifically, highvalues of DR produce more clearly distinct lobes (or hills) inthe histogram; the more distinct these lobes, the higher theimage contrast. The overall impression of lower intensities inhigh DR values may be due to the fact that the gain wasnot allowed to change in the context of this study. In Fig. 3,

2106 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 7, JULY 2009

Fig. 4. Example of B-mode ultrasound image of the carotid (a) before and (c) after normalization. The rectangular regions in the lumen and the adventitia wereused as reference in the normalization procedure. The histograms of the selected ROI in the arterial wall before and after image normalization are shown in (b)and (d), respectively.

in addition to image histograms, examples of the frequency-domain representation of the images, which were obtainedthrough 2-D fast Fourier transform, are shown. As we can seein this case, the effect of persistence is minimal, if any, on notonly image intensities but also intensity frequencies. This maybe due to the fact that a high frame rate (25 Hz) is used, whichmay compensate for the frame-averaging effect.

B. Image Normalization

The need for image normalization has been suggested in thepast, and normalization using blood echogenicity as a referencepoint has been applied in ultrasound images of the carotid artery[9]. Image normalization aims at minimizing variability intro-duced by different equipment, operators, and gain settings, andcan, thus, facilitate tissue comparability. This does not qualifynormalization as a mitigating factor for calibration errors; itmerely allows comparability of images acquired using differentsettings. The use of different settings, aiming at producingimages that are easy to interpret, cannot be considered ascalibration error.

The images were manually normalized by linearly adjustingthe image, so that the median gray-level value of the blood wasin the range of 0–5 and the median gray level of the adventitiawas in the range of 180–190, according to widely accepted

specifications [3]. The gray levels of the images ranged from0 to 255. Fig. 4(a) shows the two regions that correspondto the blood and adventitia. The regions were selected onthe image that was recorded using the reference values toperform normalization. The histograms for the selected ROIfor the image before and after normalization are shown inFig. 4(b) and (d), respectively. As we can see, the normaliza-tion process resulted in a linear increase in the image pixelintensities.

C. Motion Analysis from Sequences of Images

Motion estimation from sequences of images was performedusing region tracking and block matching. The method isdescribed in detail in [5]. Briefly, an ROI can be selectedin the first frame of the sequence, and its position is auto-matically tracked in subsequent frames. Automatic trackingis based on matching the ROI pixel intensities in each framewith those in the first frame. Rectangular ROIs measuring3.5 × 2.5 mm2 (35 × 25 pixels2) were tracked (Fig. 1). TheROI size was selected according to the recommendations in [5].The results of the motion analysis consist of waveforms show-ing radial and axial displacements, as well as the correlationcoefficient (CC). The CC is a measure of the match betweenROIs in subsequent frames, and its values range from 0 to 1.

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Fig. 5. Examples of radial displacements for different values of (a) DR and (b) persistence, and (c) before and after image normalization.

To quantify the findings of motion analysis and comparethe results obtained with different settings, as well as beforeand after image normalization, objective indexes were extractedfrom each waveform. The variance was used as an index of thedegree of deviation of each waveform from its average valueand was defined as

VW =1

N − 1

N∑

n=1

(W (n) − W (n)

)2(1)

where N is the total number of frames within the sequence,W (n) is the value of the waveform (radial or axial position) atframe n (corresponding to time t = n/f , where f = 25 Hz isthe frame rate), and W (n) is the average value of W (n).

The mean value of the CC was used as an index of thereliability of motion analysis.

In radial displacement waveforms, which are distinctivelyperiodic, the time delay between cardiac end-systole and maxi-mal wall displacement was also estimated. The exact time whencardiac end-systole occurred was obtained by the electrocardio-gram (ECG), which was simultaneously recorded with imagesequences.

Differences between indexes of waveforms obtained withdifferent scanner settings were assessed by the Student’s t-test.A value of p < 0.05 was considered significant.

III. RESULTS

Figs. 5–7 show examples of the effect of DR and persistenceon the radial displacements, axial displacements, and CCs,respectively, for the ROI shown in Fig. 2. Table I shows themean values (±std) of the variances of the radial and axial dis-placement waveforms. Table II shows the mean values (±std)of the CCs for different values of the DR and persistence.Because the differences were not significant, no quantitativeindication of statistical significance is shown.

An increase in the DR did not significantly change thevariance of the radial and axial displacement waveforms. In onecase, a very low value of the DR (0 dB) caused a failure ofthe motion analysis algorithm in estimating axial motion; thisis the reason for the particularly high value of variance corre-sponding to 0 dB in Table I (posterior wall–lumen interface).An increase in persistence decreased the variance of radialdisplacements, but this change was not statistically significant.In the corresponding waveforms, this is indicated by a reductionin the amplitudes of some of the peaks corresponding to systole(Fig. 5). Image normalization did not affect the variance ofradial and axial displacements (Table I). As we can see, thecorresponding waveforms are similar [Figs. 5(c) and 6(c)].

The CCs slightly increased as the DR and persistence in-creased (Table II), but this change was not statistically sig-nificant. Table III shows the time delays between end-systole,

2108 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 7, JULY 2009

Fig. 6. Examples of axial displacements for different values of (a) DR and (b) persistence, and (c) before and after image normalization.

based on the ECG, and the peaks of radial displacements. Anincrease in persistence increased the delay, as shown in thewaveforms of Fig. 5(b). This was expected since an increase inthe number of consecutive frames used in the averaging proce-dure reduces the temporal resolution. The CCs were reasonablyhigh, i.e., greater than 0.90, for all scanner settings (Fig. 6).

IV. DISCUSSION

Ultrasound scanner settings may change the appearance ofanatomical structures and, therefore, may affect the results ofimage-processing tasks. In this study, the effect of two scannersettings, i.e., DR and persistence, as well as the effect ofimage normalization on the findings of motion analysis basedon block matching, was investigated. Changes in DR alterpixel intensities and image contrast, and this may affect theresults of image-processing techniques based on pixel graylevels, e.g., texture analysis or segmentation methods. However,the analysis of motion of tissue regions is not expected todepend on changes in pixel intensities. We, therefore, selectedto investigate whether changes in DR affect the results ob-tained with our motion estimation algorithm. The persistenceis related to an averaging of consecutive image frames and,consequently, has a smoothing effect on image appearance.This smoothing effect may introduce delays in the occurrence

of physiological events and cancel unwanted speckle patterns,which would, otherwise, be detected by the motion analysisalgorithm.

A relatively small number of image ROIs were interrogatedin this study. This was mainly due to practical difficultieswith performing similar studies during routine clinical prac-tice, which would allow the investigation of elderly/diseasedsubjects. In an attempt to cover a somewhat larger range ofclinical scenarios, we interrogated image areas that correspondto different anatomical structures and are characterized bydifferent intensity patterns.

The main differences between healthy and pathologicalcases, in terms of imaged anatomical structures, lie in geomet-rical and echogenicity features. More specifically, the healthyarterial wall has an almost linear (low curvature) appearance.In the presence of disease, i.e., atherosclerotic plaque, the wallbecomes curved, protruding into the lumen, due to wall thick-ening caused by the accumulation of atherosclerotic material(lipids, fibrous tissue, etc.). The echogenicity of the healthywall has a characteristic pattern with high values in the intimal(innermost) and adventitial (outermost) layers. The echogenic-ity of the diseased wall is altered (it usually becomes lower)due to altered wall composition. As a result of this and alsobecause curved geometry affects ultrasound interaction withbiological tissue, arterial wall boundaries may become fuzzier

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Fig. 7. Examples of CCs for different values of (a) DR and (b) persistence, and (c) before and after image normalization.

than in normal cases. Because the block-matching-based mo-tion analysis methodology used in this paper employs a verysmall ROI, tissue geometry does not affect the results. On theother hand, echogenicity patterns may affect motion analysis,which is based on image intensities.

Image normalization is an important preprocessing task inthe analysis of ultrasound images. It allows comparability ofimages obtained with different equipment and/or sonographersusing different scanner settings, some of which, including gainand contrast, are subjectively defined to obtain a reasonableimage of the anatomical structures. In particular, image nor-malization has been recommended and widely used in theanalysis of the texture of sonographic images of the carotidartery [3]. The study of the potential effect of normalization onmotion analysis was considered important, particularly whencarotid artery ultrasound image analysis includes both textureand motion analyses. According to our results, normalizationdid not affect the estimation of motion of arterial tissue areas.Therefore, reliable motion analysis may be performed in non-normalized images.

The variance was used as an index of the degree of move-ment in radial and axial directions. Variance was preferredover amplitude, which is not easy to estimate in nonperiodicwaveforms, such as axial displacements. Changes in the DRdid not significantly affect the variances of axial and radial

displacements. Because changes in pixel intensities should notaffect the estimation of motion, this was an indication of therobustness of the motion analysis algorithm based on blockmatching. It should be pointed out that high values of DRcontain more image information, which is useful for image-processing techniques that may be used in combination withmotion analysis, e.g., texture analysis.

Changes in persistence did not also significantly affect thevariances of axial and radial displacements. In some cases, highvalues of persistence caused a decrease in the peak displace-ments; this may be due to the smoothing effect of persistence.However, an increase in persistence caused a significant delaybetween cardiac end-systole and peak radial displacement. Thiswas expected, because an increase in the number of consecutiveframes that are averaged introduces delays in the detection ofphysiological events, such as systole.

Image normalization did not significantly affect the variancesof radial and axial displacements. Axial displacements, how-ever, were slightly more sensitive to this type of preprocessing(Table I).

The study presented in this paper has shown that, althoughscanner settings may not affect motion analysis, they may beimportant in the study of physiology; high persistence valuesintroduce significant delays in arterial wall movements, com-pared with myocardium. Consequently, scanner settings should

2110 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 7, JULY 2009

TABLE IVARIANCE OF RADIAL DISPLACEMENTS (VRD) AND AXIAL DISPLACEMENTS (VAD) FOR DIFFERENT VALUES OF DR AND PERSISTENCE

carefully be selected for a valid interpretation of findings. Possi-ble research perspectives in this field may be orientated towardtwo distinct directions: First, from an engineering viewpoint, alarger scale study investigating and appropriately quantifyingthe effect of a number of settings on not only the analysisof motion but also other image-processing tasks, which areoften applied to ultrasound images of the carotid artery, wouldbe interesting. Such tasks include the analysis of the textureand segmentation of the arterial wall boundary. Second, froma physician/sonographer viewpoint, it might be of interest toinvestigate the effect of settings values on the subjective (visual)evaluation of ultrasound images by different operators. Thecombined information provided by the two research orien-tations would be useful for accurate interpretation of find-ings related to diagnostic indexes and physiological studies.An additional potential direction along this line of researchwould be the development of a “physical transfer function”

that relates various physical processes, which are involved invarious clinical scenarios, with a number of ultrasound de-tected imaging signal parameters. This would be useful to thedesign of enhanced ultrasound systems for dedicated medicalapplications.

V. CONCLUSION

In conclusion, scanner settings are important not only inthe visualization but also in the processing of medical imagesand should be taken into account in image interpretation tasks.Based on the findings of this study, it is suggested that highvalues of DR (66 or 90 dB) and low values of persistence(0 or 5.6) be used for motion analysis based on block matching.It is also important that, in clinical protocols including motionanalysis, standardized techniques using the same machine set-tings are adopted. Image normalization was not shown to affect

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TABLE IIMEAN (±std) VALUES OF THE CCs FOR DIFFERENT VALUES OF

DR AND PERSISTENCE

TABLE IIITIME DELAY BETWEEN END-SYSTOLE, BASED ON ECG, AND PEAKS OF

RADIAL WAVEFORMS FOR DIFFERENT VALUES OF PERSISTENCE.(†) p < 0.05 COMPARED TO VALUES FOR PERSISTENCE = 0. (∗) p < 0.05

COMPARED TO CORRESPONDING VALUES FOR POSTERIOR

WALL-LUMEN INTERFACE

the results of motion analysis, which was an indication of therobustness of the motion analysis algorithm.

REFERENCES

[1] J. Bang, T. Dahl, A. Bruinsma, J. H. Kaspersen, T. A. N. Hernes, andH. O. Myhre, “A new method for analysis of motion of carotid plaques fromRF ultrasound images,” Ultrasound Med. Biol., vol. 29, no. 7, pp. 967–976,Jul. 2003.

[2] I. Christodoulou, C. S. Pattichis, M. Pantziaris, and A. Nicolaides,“Texture-based classification of atherosclerotic carotid plaques,” IEEETrans. Med. Imag., vol. 22, no. 7, pp. 902–912, Jul. 2003.

[3] T. Elatrozy, A. Nicolaides, T. Tegos, A. Zarka, M. Griffin, and M. Sabetai,“The effect of B-mode ultrasonic image standardization on the echogenic-ity of symptomatic and asymptomatic carotid bifurcation plaques,”Int. Angiol., vol. 17, no. 3, pp. 179–186, Sep. 1998.

[4] G. Geroulakos, G. Ramaswami, A. N. Nicolaides, K. James,N. Labropoulos, G. Belcaro, and M. Holloway, “Characterisation ofsymptomatic and asymptomatic carotid plaques using high-resolutionreal-time ultrasonography,” Br. J. Surg., vol. 80, no. 10, pp. 1274–1277,Oct. 1993.

[5] S. Golemati, A. Sassano, M. J. Lever, A. A. Bharath, S. Dhanjil, andA. N. Nicolaides, “Carotid artery wall motion estimated from B-modeultrasound using region tracking and block-matching,” Ultrasound Med.Biol., vol. 29, no. 3, pp. 387–399, Mar. 2003.

[6] P. R. Hoskins and W. N. McDicken, “Colour ultrasound imaging of bloodflow and tissue motion,” Br. J. Radiol., vol. 70, no. 837, pp. 878–890,Sep. 1997.

[7] L. Hykes, W. R. Hedrick, and D. E. Starchman, Ultrasound Physics andInstrumentation. New York: Churchill Livingstone, 1985.

[8] S. Meairs and M. Hennerici, “Four-dimensional ultrasonographiccharacterization of plaque surface motion in patients with symptomaticand asymptomatic carotid artery stenosis,” Stroke, vol. 30, no. 9, pp. 1807–1813, Sep. 1999.

[9] J. E. Wilhjelm, M. L. M. Grønholdt, B. Wiebe, S. K. Jespersen,L. K. Hansen, and H. Sillesen, “Quantitative analysis of ultrasound B-modeimages of carotid atherosclerotic plaque: Correlation with visual classifica-tion and histological examination,” IEEE Trans. Med. Imag., vol. 17, no. 6,pp. 910–922, Dec. 1998.

Spyretta Golemati (M’01) received the Diploma inmechanical engineering from the National TechnicalUniversity of Athens, Athens, Greece, in 1994 andthe M.Sc. and Ph.D. degrees in bioengineering fromImperial College of Science, Technology and Medi-cine, University of London, London, U.K., in 1995and 2000, respectively.

She is currently a Lecturer in biomedical engineer-ing with the National and Kapodistrian Universityof Athens, Athens. She has coauthored 25 papers ininternational refereed journals and chapters in books,

and 34 papers and abstracts in international refereed conference proceedings.Her research interests include medical imaging, image and signal processing,and respiratory mechanics.

Dr. Golemati is a member of the Technical Chamber of Greece.

John S. Stoitsis (S’05) received the Diploma inelectrical and computer engineering from AristotleUniversity of Thessaloniki, Thessaloniki, Greece, in2002 and the M.Sc. and Ph.D. degrees in biomedicalengineering from the University of Patras, Patras,Greece, in 2004 and 2007, respectively.

He is currently with the Biomedical Simulationsand Imaging Laboratory, School of Electrical andComputer Engineering, National Technical Univer-sity of Athens, Athens, Greece. He has authoredor coauthored seven papers in international journals

and chapters in books, and more than ten papers in international confer-ence proceedings. His research interests include biomedical image and signalprocessing, medical informatics, computer-aided diagnosis, and neuroscience.

Dr. Stoitsis is a member of the Technical Chamber of Greece.

2112 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 58, NO. 7, JULY 2009

Dimitrios A. Perakis was born in Athens, Greece,in 1981. He received the Diploma in applied math-ematics and physical sciences from the NationalTechnical University of Athens, Athens, in 2004 andthe M.Sc. degree in biomedical engineering from theUniversity of Patras, Patras, Greece, in 2006.

He is currently working toward the Ph.D. degreewith the Biomedical Simulations and Imaging Lab-oratory, School of Electrical and Computer Engi-neering, National Technical University of Athens.In 2003, he participated in the Summer Student

Program of the European Organization for Nuclear Research (CERN), witha university-granted scholarship. His research interests include computationalneuroscience, focusing on the anatomical and physiological modeling of thebasal ganglia.

Emily Varela received the degrees from theTechnological Educational Institution of AthensNursing School, Athens, Greece, in 2000 and fromAthens University Nursing School, Athens, in 2007.She is currently working toward the Ph.D. degreein molecular medicine at the University of Athens,Athens.

She is currently a Technician with the BiomedicalResearch Foundation, Academy of Athens, Athens,where she works on human and transgenic murinemodels. She authored three publications in interna-

tional journals and two papers in a Greek journal.Ms. Varela was the recipient of two awards for her participation in two

European courses.

Anastasia Alexandridi, photograph and biography not available at the time ofthe publication.

Constantinos H. Davos received the DoctorateDiploma from the University of Athens, Athens,Greece, in 1998. In 1998, he became a board-certified cardiologist. From 1999 to 2001, he wasa Research Fellow in clinical cardiology with theRoyal Brompton Hospital and Cardiac Medicine,National Heart and Lung Institute, London, U.K.Since 2003, he has been an Assistant Professor withthe Cardiovascular Research Laboratory, BiomedicalResearch Foundation, Academy of Athens, Athens.He has also been a Consultant Cardiologist in major

pharmaceutical companies and clinical research organizations. He has receivedresearch grants from the Hellenic Society of Cardiology (HSC) in 2000 and2005, the Imperial College School of Medicine in 2001, and the HellenicGeneral Secretariat of Research and Technology in 2005. He authored 21peer-reviewed publications in major cardiovascular papers and 56 abstractpresentations in international meetings. His clinical work includes studies onchronic heart failure and adult congenital heart diseases. His research interestsinclude cardiomyopathies and ischaemia in animal models.

Prof. Davos is a Fellow of the European Society of Cardiology (ESC) and amember of the International Society of Heart Research and the Heart FailureAssociation. He was the recipient of research awards from the HSC and ESCin 2003.

Konstantina S. Nikita (M’96–SM’00) received theDiploma degree in electrical engineering and thePh.D. degree from National Technical Universityof Athens (NTUA), Athens, Greece, in 1986 and1990, respectively, and the M.D. degree from theUniversity of Athens, Athens, in 1993.

Since 1990, she has been a Researcher with theInstitute of Communication and Computer Systems,NTUA. In 1996, she joined the School of Electricaland Computer Engineering, NTUA, where she iscurrently a Professor. She has authored or coauthored

90 papers in refereed international journals and chapters in books, and morethan 150 papers in international conference proceedings. She was a coauthorof one book in Greek and a coeditor of one book in English published bySpringer. She is the holder of two Greek patents. She has been the TechnicalManager of several European and national research and development projectsin the field of biomedical engineering. Her current research interests includemedical imaging, biomedical signal and image processing and analysis, bio-medical informatics, health telematics, simulation of physiological systems,computational bioelectromagnetics, biological effects, and medical applicationsof electromagnetic waves.

Dr. Nikita is a member of the Technical Chamber of Greece, the AthensMedical Association, and the Hellenic Society of Biomedical Engineering.She was the recipient of the 2003 Bodossakis Foundation Academic Prizefor exceptional achievements in “Theory and Applications of InformationTechnology in Medicine.”