ultrasonic imaging of carotid atherosclerosis

ULTRASONIC IMAGING OF CAROTIDATHEROSCLEROSIS

CONSTANTINOS S. PATTICHIS

CHRISTODOULOS

CHRISTODOULOU

University of CyprusNicosia, Cyprus

EFTHYVOULOS KYRIACOU

MARIOS PANTZIARIS

ARDREW NICOLAIDES

Cyprus Institute of Neurologyand Genetics

Nicosia, Cyprus

MARIOS S. PATTICHIS

University of New MexicoAlbuquerque, New Mexico

CHRISTOS LOIZOU

Intercollege Limassol CampusLimassol, Cyprus

1. INTRODUCTION

According to the 2003 World Health Report, cardiovascu-lar disease made up 16.7 million, or 29.2%, of total globaldeaths. Of the 16.7 million deaths from cardiovasculardisease every year, 7.2 million are because of ischaemicheart disease, 5.5 million to cerebrovascular disease, andan additional 3.9 million to hypertensive and other heartconditions. Moreover, at least 20 million people surviveheart attacks and strokes every year. It is estimated thatby 2010, cardiovascular disease will be the leading causeof death in developing countries. Ultrasound imaging pro-vides a well-established technique in the diagnosis andassessment of cardiovascular disease. As a result of itsnoninvasive nature, its continuing advances in ultrasoundtransducer instrumentation, and its digital image pro-cessing technology, vascular imaging is progressivelyachieving a more important role in helping the physicianvisualize the morphology of vascular structure, as well asmeasure blood flow velocity, arterial wall changes, andtexture of atherosclerotic plaque.

2. HISTORICAL REVIEW

Ultrasound was originally used for industrial purposes,and its value as a diagnostic tool was initially recognizedin the late 1940s. Some of the pioneer researchers in usingultrasound for medical diagnosis were Dr. K. Tanaka, Dr.T. Wagai, Dr. Y. Kikuchi, S. Satomura, and Dr. Y. Nimurain Japan; Dr. K. T. Dussik in Europe; and Dr. G. D. Lud-wig, Dr. R. R. Bolt, Dr. T. Heuter, Dr. J. J. Wild, Dr. J. M.Reid, Dr. D. Howry, and Dr. W. J. Fry in the United States(1,2).

Ultrasound imaging became an accepted imaging diag-nostic technique in the early 1970s when grayscale ultra-sonography (i.e., the formation of images from back-scattered ultrasound) was introduced. The informationcarried out by returning ultrasound echoes, affected by

tissue intervening between the ultrasound probe and thetarget of interest, is used to differentiate the differenttypes of tissue. The demonstration that the cancerousstomach wall could be differentiated from normal tissuebased on the echo patterns, performed by Wild and Reid,triggered the investigation of tissue characterization re-search in the 1970s (2). Following this work, many studieswere performed in order to identify and study the mech-anism of ultrasound when transmitted through the hu-man body, achieving significant progress in theunderstanding of ultrasound tissue interaction and char-acterization (3).

The progress of ultrasound technology made possiblethe introduction of noninvasive techniques in imagingstructural and functional abnormalities in large and smallvessels with high accuracy, which helped the diagnosis ofpatients at risk from several vascular pathologies (3). Ul-trasound is currently a standard technique for screeningpatients at risk for atherosclerosis in the absence of clin-ical symptoms or for a detailed diagnosis of symptomaticsubjects.

3. BASIC PRINCIPLES OF ULTRASOUND

Ultrasound is a sound wave with frequency that exceeds20KHz. It transports energy and propagates through sev-eral means as a pulsating pressure wave. It is described bya number of wave parameters such as pressure density,propagation direction, and particle displacement. If theparticle displacement is parallel to the propagation direc-tion, then the wave is called longitudinal or a compressionwave. If the particle displacement is perpendicular to thepropagation direction, the wave is called shear or trans-verse wave. Interaction of ultrasound waves with tissue issubject to the laws of geometrical optics. It includes re-flection, refraction, scattering, diffraction, interference,and absorption. Except for interference, all other interac-tions reduce the intensity of the ultrasound beam.

The main characteristic of an ultrasound wave is thewavelength l, which is a measure of the distance betweentwo adjacent maximum or minimum values of a sinecurve, and frequency f, which is the number of wavesper unit of time. The product of these two measures givethe velocity of ultrasound wave propagation, v, describedwith the equation v¼ fl. Ultrasound techniques aremainly based on measuring the echoes transmitted backfrom a medium when sending an ultrasound wave to it.Mainly, two principles are used in ultrasound diagnostics,the echo-impulse technique and the Doppler technique.

In the echo-impulse ultrasound technique, when thevelocity of ultrasound for a particular medium and fre-quency are known, then the depth of the echoes’ reflectioncan be measured. In these techniques, pulses of ultra-sound waves are transmitted to the medium and the re-flections from the medium are measured in order to getthe image. The two main scanning modes are A and Bmodes. A mode refers to amplitude mode scanning. In thismode, the strength of the detected echo signal is measuredand displayed as a continuous signal in one direction. Thisscanning technique has the limitation that the recorded

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signal is 1-D with limited anatomical information. A modeis no longer used, especially for the assessment of cardio-vascular disease. B mode refers to brightness mode. In Bmode, echoes are displayed as a 2-D image, also referred toas 2-D tomography. The amplitude of the returning echoesis represented as dots (pixels) of an image with differentgray values or colors (Fig. 1). The image is constructed bythese dots line by line.

The quality of the produced image depends on axial andlateral image resolution. Resolution is defined as thesmallest distance between two points at which they canbe represented as distinct. Axial resolution refers to theability of representing two points that lie along the direc-tion of ultrasound propagation. It depends on the wave-length of the beam. In B mode, ultrasound pulses consistof one to two sinusoidal wavelengths, and the axial reso-lution is dependent on the wavelength of the waveforms.Resolution depends on the frequency of the beam wave-forms. Lateral resolution refers to the ability to resolvetwo points that lie at a right angle to the direction of ul-trasound propagation, which is dependent on the fre-quency of ultrasound and the width of the ultrasoundwave (beam) (3).

Speckle is another important factor affecting the qual-ity of ultrasound B-mode imaging. It is described as anultrasonic textural pattern that varies depending on thetype of biological tissue. Tissue pathology that causeschanges in the anatomical structure of tissue might alsoresult in a change in its speckle ultrasonic appearance.The presence of speckle may obscure small structures,thus degrading the spatial resolution of an ultrasonic im-age.

In vascular ultrasound imaging, in order to achieve thebest results, the transmission frequencies are in the rangeof 1 to 10MHz. The selected frequency depends on theapplication domain. For arteries located close to the hu-man skin, frequencies greater than 7.5MHz are used,whereas for arteries located deeper in the human body,frequencies from 3 to 5MHz are used. For the carotid bi-furcation, frequencies in the range of 5 to 12MHz areused. For transcranial applications, frequencies less than2MHz are used. When selecting a frequency, the user hasto keep in mind that axial resolution is proportional to theultrasound wavelength; whereas the intensity of the sig-

nal depends on the attenuation of the signal transmittedthrough the body, with the higher the frequency thehigher the attenuation. Therefore, a trade-off exists be-tween higher-resolution ultrasound images at smallerdepth and lower-resolution images at higher depths.

The second principle used in ultrasound diagnostics isthe Doppler principle, named after the physicist ChristianDoppler (1803–1853) (4). This technique is based on theprinciple that the perceived frequency of sound echoes re-flected by a moving target is related to the velocity of thetarget. The frequency shift (Doppler shift) Df of the echosignal is proportional to the flow velocity v (cm/s) and theultrasound transmission frequency f (MHz). The Dopplershift is described by the formula Df¼ 2f0(v cos y)/c, whereDf is the Doppler frequency shift, f0 is the transmittedfrequency of the signal, v is the speed of the movement ofthe scatterer, y is the angle between the direction of move-ment of the moving object and the ultrasound beam, and cis the speed of sound through tissue that is approximately1540m/s.

In Doppler imaging, the returned echoes are displayedas a 2-D signal, as shown in Fig. 2. When blood flow in avessel is being examined, sound reflections caused by theblood’s corpuscular elements play a major role. Based onthe fact that blood flow velocity varies in different areas ofa vessel and because of the turbulent flow, the Dopplersignal contains a broad frequency spectrum. In normalinternal carotid artery, the spectrum varies from 0.5KHzto 3.5KHz, and v is less than 120 cm/s if an ultrasoundbeam of 4MHz is used.

Several types of Doppler systems are used in medicaldiagnosis, continuous wave (CW) Doppler, pulsed wave(PW) Doppler, duplex ultrasound, and Color Flow Duplex.These types of Doppler systems are briefly presented be-low.

In CW Doppler, the machine uses two piezoelectric el-ements serving as transmitters and receivers. They trans-mit ultrasound beams continuously. As a result of thecontinuous way that ultrasound is being transmitted, nospecific information about depth can be obtained. PWDoppler is used in order to detect blood flow at a specificdepth. In this technique, a single piezoelectric element isused as the transmitter and the receiver. Sequences ofpulses are transmitted to the human body that are gated

4. Internal carotid

3. External carotid

2. Bifurcation

1. Common carotid

Figure 1. Ultrasound B mode longitudinal im-age of the carotid bifurcation.

2 ULTRASONIC IMAGING OF CAROTID ATHEROSCLEROSIS

for a short period of time in order to receive the echoes. Byselecting the time interval between the transmitted andreceived pulses, it is possible to examine vessels at a spe-cific depth.

Duplex ultrasonography is a combination of continuousor pulsed wave Doppler techniques integrated with anecho-impulse technique. It provides tissue imaging as wellas blood flow characteristics. The advantages and disad-vantages of each system can be used to decide the choice ofthe specific Doppler method to be used for each of the var-ious diagnostic procedures. The PW technique is mostcommon whereas the CW technique is used to detecthigh blood flow velocity, which is used in order to studydeep-lying areas relevant to cardiology or velocity pat-terns related to stenosis problems.

Color-coded duplex imaging systems are commonlyused in clinical diagnosis. Doppler shifts from moving par-ticles displayed in color provide flow information through-out the region of interest (grayscale field). The detectedvelocity scale of the moving targets is determined by thepulse repetition frequency of the ultrasound pulses. Thesecolor flow systems detect and process amplitude, phase,and frequency of the returning echoes. The use of thepulse echo imaging enables the use of Doppler ultrasoundin order to denote velocity and flow direction using colorcodes. The color image is superimposed on a grayscale B-scan image, thus resulting in the final color flow dupleximage as shown in Fig. 3. The mean velocity at each pointis displayed in a color-coded scale.

4. ULTRASOUND VASCULAR IMAGING

Ultrasound is widely used in vascular imaging because ofits ability to visualize body tissue and vessels in a nonin-vasive and harmless way and to visualize, in real-time, thearterial lumen and wall, something that is not possiblewith any other imaging technique. B-mode ultrasound

imaging can be used in order to visualize arteries repeat-edly from the same subject in order to monitor the devel-opment of atherosclerosis. Monitoring of the arterialcharacteristics like the vessel lumen diameter, the intimamedia thickness (IMT) of the near and far wall, and themorphology of atherosclerotic plaque are very importantin order to assess the severity of atherosclerosis and eval-uate its progression (5).

A typical longitudinal image from an adult normal sub-ject can be seen in Fig. 1. The morphology of B-mode ul-trasound image is also being described in this figure. Aclose view of the intimamedia thickness is shown in Fig. 4,where the near and far walls of the artery are depicted bya double line pattern, where the two bright lines corre-sponding to echogenic lumen-intima and media-adventitiaare separated by a sonolucent region. The carotid arterywas among the first peripheral vessels that were studiedusing B-mode ultrasound, with the carotid IMT changesfound to be associated with several risk factors for ath-erosclerosis.

The arterial wall changes that can be easily detectedwith ultrasound are the end result of all risk factors (ex-ogenous, endogenous, and genetic) known and unknownand are better predictors of risk than any combination ofconventional risk factors. Extracranial atherosclerotic dis-ease, known also as atherosclerotic disease of the carotidbifurcation, has two main clinical manifestations: (a)asymptomatic bruits and (b) cerebrovascular syndromessuch as amaurosis fugax, transient ischaemic attacks(TIA), or stroke, which are often the result of plaque ero-sion or rapture with subsequent thrombosis producing oc-clusion or embolization (6,7).

Carotid plaque is defined as a localized thickening in-volving the intima and media in the bulb, internal carotid,external carotid, or common femoral arteries (Fig. 5). Re-cent studies involving angiography, high-resolution ultra-sound, thrombolytic therapy, plaque pathology,coagulation studies, and more recently, molecular biology

1.

2.

Figure 2. Longitudinal color flow duplex image ofthe carotid artery combined with Doppler ultra-sound image. Highlighted image with white con-tour on top shows the carotid bifurcation. The 2-Dsignal shows the velocity variation related to thecardiac cycle. Blood flow velocity spectrum is dis-played with markings 1 and 2, where marking 1represents the peak systolic velocity and marking 2represents the end diastolic velocity, which is theduration of one cardiac cycle.

ULTRASONIC IMAGING OF CAROTID ATHEROSCLEROSIS 3

have implicated atherosclerotic plaque rapture as a keymechanism responsible for the development of cerebro-vascular events (8–10).

Atherosclerotic plaque rapture is strongly related tothe morphology of the plaque (11). The development andcontinuing technical improvement of noninvasive high-resolution vascular ultrasound enables the study of thepresence, rate of progression or regression, and, most im-portantly, consistency of plaques. The ultrasonic charac-

teristics of unstable (vulnerable) plaques have beendetermined (12,13), and populations or individuals at in-creased risk for cardiovascular events can now be identi-fied (14). In addition, high-resolution ultrasound enablesthe identification of the different ultrasonic characteristicsof unstable carotid plaques associated with amaurosis fu-gax, TIAs, stroke, and different patterns of CT-brain in-fraction (12,13). This information has provided newinsight into the pathophysiology of the different clinicalmanifestations of extracranial atherosclerotic cerebrovas-cular disease using noninvasive methods.

Different classifications have been proposed in the lit-erature for the characterization of atherosclerotic plaquemorphology, resulting in considerable confusion. For ex-ample, plaques containing medium or high level uniformechoes were classified as homogeneous by Reilly (15) andcorrespond closely to Johnson’s dense and calcified pla-ques (16), to Gray–Weale’s type 3 and 4 (17), and to Wi-dder’s type I and II plaques (18) (i.e., echogenic orhyperechoic). A recent consensus on carotid plaque char-acterization has suggested that echodensity should reflectthe overall brightness of the plaque with the term hypo-echoic referring to echolucent plaques (19). The referencestructure to which plaque echodensity should be comparedwith is, for hypoechoic plaques, blood; for the isoechoic, the

Figure 3. Color-coded duplex longitudinal image ofthe carotid bifurcation (red or blue color indicatesthe blood flow direction along the ultrasound beamtoward or away from the transducer). For the cur-rent image, red represents the blood flow directiontoward the transducer, whereas blue represents theblood flow direction away from the transducer.Doppler direction is tilted as shown in the parallel-ogram. The relative direction from the blood flowand ultrasound beam direction determines the color.

1 32

Figure 4. Close view of manual measurement of the Intima Me-dia Thickness, IMT: 1. 0.9mm, 2. 0.8mm, 3. 0.86mm.


sternomastoid muscle; and for the hyperechoic ones, thebone of the adjacent cervical vertebrae.

Enough published evidence exists to support the clin-ical usefulness of ultrasonic plaque characterization, pa-tients with hypoechoic carotid plaques being at increasedrisk of stroke. Polak has recently investigated the associ-ation between stroke and internal carotid artery plaqueechodensity (20). In this study, plaque morphology hasbeen subjectively characterized as hypoechoic, isoechoic,or hyperechoic in relation to the surrounding soft tissues.The stroke rate for hypoechoic plaques was 2.78 timeshigher than for isoechoic and hyperechoic plaques. In ad-dition to the subjective characterization of plaques, stud-ies that presented computer-assisted plaquecharacterization using ultrasound B-mode images of pla-ques taken from a duplex scanner with fixed instrumentsettings, including gain and time control, have been pub-lished. In a study by El-Barghouty et al., the median of thefrequency distribution of grayscale values of the pixelswithin the plaque is used as the measurement of echo-density (21).

5. IMAGE ANALYSIS

Visual assessment of vascular images or video on the mon-itor of the ultrasound machine is widely used in clinicalpractice. Modern digital image analysis techniques facil-itate the possibility of extracting additional information inquantitative form, enabling quantitative image analysis,and, subsequently, computer-aided diagnosis. The overallobjective of quantitative image analysis including com-puter-aided diagnosis is to enable early diagnosis, diseasemonitoring, and better treatment. The advantages ofquantitative image analysis and computer-aided diagno-sis systems can be summarized as follows:

* Standardization. Diagnoses obtained from differentlaboratories using similar criteria can be verified.

* Sensitivity. Findings on a particular subject may becompared with a database of normal values or a de-cision can be made by an imaging system decidingwhether an abnormality exists.

* Specificity. Findings may be compared with da-tabases for various diseases or a decision can bemade by the imaging system with respect to thetype of abnormality.

* Equivalence. Results from a series of examinations ofthe same patient may be compared to decide whetherevidence of disease progression or of response totreatment exists. In addition, the findings of differ-ent imaging systems can be compared to determinewhich are more sensitive and specific.

* Efficacy. The results of different treatments can bemore properly evaluated.

In most cases, the agreement of quantitative medicalimaging analysis with visual assessment is a prerequisitefor its acceptance. As a result of the complexity of thequantitative analysis of vascular imaging, a series of pro-cesses have to be followed, such as despeckle filtering,segmentation, feature extraction and selection, and clas-sification. These processes are briefly described in thissection.

The major performance-limiting factor in visual lesiondetection in ultrasound imaging is the speckle noise thatmakes the signal or lesion difficult to detect and diagnoseby a physician (22). Speckle is a multiplicative noise thatreduces image contrast and detail resolution, degradestissue texture, reduces the obstruction of small low-con-trast lesions, and makes continuous structures appeardiscontinuous. Different speckle techniques have been in-troduced in the literature that are based on local statisticswhere a moving window is applied to the image using themean and variance (22), linear scaling of the gray levelvalues (23), the most homogeneous neighborhood aroundeach pixel (24), geometric filtering (25), homomorphic fil-tering (26), anisotropic and speckle anisotropic diffusion(27), and wavelet filtering (28). In a recent comparativestudy of despeckle filtering techniques evaluated in alarge number of asymptomatic and symptomatic ultra-sound images of the carotid artery (29), it was shown thatthe best filters were the local statistics one based on amoving window 5 � 5 using the mean and variance andthe homogeneous mask area filter using the speckle index(that is defined as the ratio of standard deviation andmean of the window). The first filter is more suitable forthe analysis of plaque morphology and texture analysis,whereas the second filter is more suitable for measuringthe intima-media thickness as well as for identifying thedegree of stenosis, and the outline of the plaque contour.Results of the first-order local statistics despeckle filterapplied in asymptomatic and symptomatic ultrasound im-ages of the carotid artery are given in Fig. 6. It is shown

(a) (b)

Figure 5. (a) Ultrasound B-mode image froman atherosclerotid plaque (outlined with closedwhite contour) found in the internal carotid. (b)Ultrasound color-coded duplex image of thesame carotid. These images help the physiciandecide about the presence, size, and morphol-ogy of plaque.


that despeckle filtering preserves the image quality andenhances the visual observation.

Segmentation in vascular imaging is one of the mostdifficult tasks in image processing. It targets to subdividean image into its constituent regions or objects. For exam-ple, in the automated segmentation of an ultrasound im-age of the carotid artery, interest lies in identifying theintima media and subsequently measure its thickness,and furthermore, determine the presence or absence of aplaque, and if a plaque exists, to determine its contour.Although in ultrasound medical imaging different seg-mentation methods were developed, only a few methodswere specifically developed for vascular imaging and morespecifically for the segmentation of the carotid artery. Themajority of the carotid artery segmentation methods de-veloped are suitable for delineating the lumen walls andthe IMT. For lumen delineation in transversal ultrasoundimaging, the Hough transform was investigated (30) aswell to find an initial approximation of the lumen area inthe left ventricle (31). Dynamic programming (32) and costfunction optimization (33) were applied for determiningthe optimal vessel wall. In intravascular ultrasound imag-ing of the carotid artery for detecting the vessel wall, thefollowing methods were developed: texture-based (34),morphology operators (35), optimal graph searching (36),and dynamic contour modeling (37).

Furthermore, snakes or deformable models to detectthe IMT in 2-D (38) and 3-D (39) ultrasound images of thecarotid artery were developed. These methods are basedon the active contour model first introduced by Kass (40),where an active contour is expressed as an energy mini-mization process, based on internal energy derived fromthe physical characteristics of the snake based on twocomponents: the continuity energy and the curvature en-ergy. In general, the snake-based methods require that theinitial contour must be drawn by an experienced ultra-sonographer, although recently a method that automati-cally detects an initial snake contour was introduced (41).

Figure 7a shows an ultrasound image of the carotidartery with automatically computed initial contours of theintima and the adventitia layers based on despeckle fil-tering and morphology operators, whereas Fig. 7b showsthe final result after the two contours were deformed us-ing the snake model (42). Thus, the IMT, an importantpredictor for myocardial infraction and stroke, can be au-tomatically computed.

To the authors knowledge, very few studies have in-vestigated the automated segmentation of atheroscleroticcarotid plaque. Figure 8 illustrates the segmentation ofatherosclerotic carotid plaque based on snakes, as well asan estimation of the initial contour of the plaque (43).

(a) (b)

(c) (d)

Figure 6. Results of despeckle filtering based onfirst-order local statistics. Asymptomatic case: (a)original, (b) despeckled. Symptomatic case: (c) origi-nal, (d) despeckled. A box indicates a region of inter-est after speckle reduction.

(a) (b)

Figure 7. Ultrasound image of the car-otid artery for an asymptomatic case: (a)automatically detected initial contours forthe IMT and (b) final contours after snakedeformation. The detected IMTaverage¼

0.82mm, IMTmaximum¼1.07mm, IMTmini-

mum¼ 0.54mm, are shown with a double line, single

line and dashed line boxes, respectively.


Following the segmentation, texture features are ex-tracted from the segmented plaque images in order to beused for the characterization of the carotid plaques. Tex-ture contains important information that is used by hu-mans for the interpretation and the analysis of manytypes of images. Although it is easy for humans to recog-nize texture, it is quite a difficult task to be defined, andsubsequently to be interpreted by digital computers.

Some of the most common texture feature algorithmsthat have been used for ultrasound texture analysis aresimple statistical descriptors (SD), spatial gray level de-pendence matrices (SGLDM) (44), gray level differencestatistics (GLDS) (45), neighborhood gray tone differencematrix (NGTDM) (46), statistical feature matrix (SFM)(47), Laws texture energy measures (TEM) (48,49), fractaldimension texture analysis (FDTA) (49,50), and Fourierpower spectrum (FPS) (45). These texture features areusually computed on a region of interest (ROI), for exam-ple, the region prescribed by the plaque contour that isautomatically or manually drawn.

Simple statistical descriptors (SD) are computed andinclude the ROI mean, median, standard deviation, skew-ness, and kurtosis values. The spatial gray level depen-dence matrices texture features as proposed by Haralicket al. (44) are the most frequently used texture features.These features are based on the estimation of the second-order joint conditional probability density functions thattwo pixels, (k, l) and (m, n), with distance d in directionspecified by the angle y, have intensities of gray level i andgray level j. Based on the probability density functions,the following texture measures and their variants (44) arecomputed: angular second moment, contrast, correlation,inverse difference moment, sum average, variance (sumand difference), and entropy (sum and difference). For achosen distance d that is usually one pixel and for angles y¼ 01, 451, 901, and 1351, four values for each of the abovetexture measures are computed. The mean and range ofthese four values are usually computed for each feature,and they are used as two different feature sets. The GLDSalgorithm (45) uses first-order statistics of local propertyvalues based on absolute differences between pairs of graylevels, or of average gray levels in order to extract the fol-lowing texture measures: contrast, angular second mo-ment, entropy, and mean. Amadasun and King (46)proposed the NGTDM in order to extract textural fea-tures, which correspond to visual properties of texture.The following features are extracted: coarseness, contrast,busyness, complexity, and strength. Fractal dimensiontexture analysis (FDTA) is based on the work of Mandelb-rot (50) who developed the fractional Brownian motion

model in order to describe the roughness of natural sur-faces. The Hurst coefficients H(k) (49) are computed fordifferent image resolutions, where a smooth texture-sur-face is described by a large value of the parameter Hwhereas the reverse applies for a rough texture-surface.The Fourier power spectrum (FPS) computes the radialand angular sum of the sample Fourier power spectrumwhere coarse texture has high values concentrated nearthe origin, and in fine texture the values are more spreadout.

Morphological image processing allows the detection ofthe presence of specific patterns, called structural ele-ments, at different scales. The selection of appropriatestructural elements for specific applications is still anopen area of research. In general, however, flat circularelements are commonly used for detecting image elementswhere no clear preferred direction exists. In contrast, lin-ear elements can be used when prior knowledge on direc-tions of interest does exist. The simplest structuralelement for near-isotropic detection is the cross ‘þ ’ con-sisting of five image pixels. Using the cross ‘þ ’ as a struc-tural element, pattern spectra are computed for eachplague image as defined in Ref. 51. After computation,each pattern spectrum is normalized. The symbol Pn,‘þ ’ isused to denote the normalized pattern spectrum at scale nusing structural element ‘þ ’. In this notation, P0,‘þ ’ al-ways denotes the original image, whereas Pn,‘þ ’,|n|40denotes the pattern spectrum value at n times the size ofthe structural element. For n40, Pn,‘þ ’ is used for detec-tion of bright patterns over a dark background, whereasfor no0, Pn,‘þ ’ is used for detection of dark patterns over abright background.

Statistical analysis of texture features was carried outfor a large number of asymptomatic and symptomatic ul-trasound images of carotid atherosclerotic plaques (52,53).It was shown that asymptomatic plaques tend to bebrighter, with less contrast; more homogeneous; smoother,with large areas with small gray tone variations; and moreperiodical, whereas in symptomatic plaques texture tendsto be darker, with higher contrast; more heterogeneous;more rough; and less periodical. Figure 9 shows the box-plots of three texture features and the range of values forthe asymptomatic and symptomatic groups. The grayscalemedian indicates how bright (high values) or dark (lowvalues) the image is on average. The entropy computedwith the SGLDM algorithm is high when the image in-tensity in neighboring pixels is more equal and smallwhen the image intensity is more unequal. Coarsenesscomputed with the NGTDM algorithm is high when largeareas with small gray tone variations are present in the

(a) (b)

Figure 8. Ultrasound image of the carotid artery: (a)plaque initial contour estimation, and (b) final plaquecontour after snakes deformation.


image and small when less local uniformity in density ex-ists. As illustrated in Fig. 9, asymptomatic plaques tend tobe brighter (higher median gray level), have higher en-tropy (i.e., the image intensity in neighboring pixels ismore equal), and be more coarse, whereas symptomaticplaques tend to be darker (lower median gray level), havelower entropy (i.e., the image intensity in neighboringpixels is more unequal), and be less coarse.

In an extensive study carried out by Polak et al. (20)where subjects were followed up for an average of 3.3years, they found that darker (i.e., hypoechoic) carotidplaques are associated with increased risk of stroke. Also,Elatrozy et al. (54) reported that plaques with grayscalemedian less than 40 are more related to ipsilateral hemi-spheric symptoms. Wilhjelm et al. (55), in a study withpatients scheduled for endarterectomy, carried out a quan-titative comparison between subjective classification ofthe ultrasound images, first- and second-order statisticalfeatures of the ultrasound imaging plaque, and a histo-logical analysis of the surgically removed plaque. Theyreported some correlation between the afore-mentionedthree types of data where the feature with the highestdiscriminatory power was contrast.

In automated quantitative methods for classifying vas-cular imaging patterns, both statistical pattern recogni-tion and artificial neural networks (ANN) were used(52,53). In statistical pattern recognition, the k-nearest-neighbor (KNN) classifier was used, whereas in ANN pat-tern recognition, the unsupervised self-organizing map(SOM) was used (56). Nine different SOM models weredeveloped, one for each texture feature set as describedabove, with the output classified into two classes: asymp-tomatic because the subject was not connected with ipsi-lateral hemispheric events or symptomatic because thesubject was connected with ipsilateral hemispheric symp-toms (52). Figure 10 illustrates the percentage of correctclassifications score of the above-mentioned texture fea-ture sets using the SOM classifier on the evaluation da-taset. The highest diagnostic performance was obtained

for the SGLDM range feature set, followed by the NGTDMand TEM feature sets. Furthermore, the outputs of theSOM classifiers were combined based on a confidencemeasure predetermined by the classifications perfor-mance of each feature set classifier. The combination ofthe classification results of the different features and thedifferent classifiers increases the probability that the er-rors of the individual features or classifiers may be com-pensated by the correct results of the rest (57). Combiningthe classification results improved the percentage of cor-rect classifications score derived by the feature sets clas-sifiers (Fig. 10). A similar combined classifier system wasimplemented using the statistical KNN classifier wherethe combined percentage of correct classifications scorewas a few percent less. Furthermore, the performance ofmorphological features for carotid plaque classificationwas also investigated (53). The findings of this studyshowed that the percentage of correct classifications scorewas similar to the texture feature sets with the highestscore (i.e., SGLDM range, NGTDM, and TEM).

6. EMERGING TECHNOLOGIES AND FUTURE TRENDS

In everyday clinical practise, the ultrasonographer ma-nipulates the transducer and mentally transforms the 2-Dimages into anatomical volume or structure, or lesion, inorder to make a diagnosis. 3-D imaging attempts to pro-vide the ultrasonographer with a more realistic recon-struction and visualization of the 3-D structure underinvestigation. In addition, 3-D imaging can provide quan-titative measurements of volume, surface distance in vas-cular anatomy, especially in pathological cases. Invascular imaging, a 3-D representation was investigatedfor the visualization of the carotid artery and the quanti-fication of the atherosclerotic plaque volume and morphol-ogy (3,39,58–61). Following the scanning of the 2-D imagesand their relative position and orientation, 3-D recon-struction is carried out by generating a 3-D representa-

Figure 9. Boxplots of three texture features (grayscalemedian, entropy, and coarseness) for class 1: asymp-tomatic and class 2: symptomatic classes. The notchedbox shows the median, lower, and upper quartiles andconfidence interval around the median. The dotted lineconnects the nearest observations within 1.5 of the in-ter-quartile range, IQR, of the lower and upper quar-tiles. Crosses (þ ) indicate possible outliers,observations more than 1.5 � IQR and 3.0 � IQRfrom the quartiles respectively.


tion of the anatomy under study by placing the acquired 2-D images in their correct relative position using two dis-tinct methods: voxel-based and feature-based reconstruc-tion (58). The voxel-based 3-D reconstruction is the mostpopular technique in ultrasound image reconstruction,where the set of acquired 2-D images together with theirposition and orientation are used to build a voxel-basedvolume (i.e., a 3-D grid of picture elements). In feature-based reconstruction, the 2-D images are segmented ei-ther manually or automatically and then the segmentedregions are classified or labeled. This information is usedto generate 3-D solid mesh representation of the organunder investigation. Following 3-D reconstruction, 3-D vi-sualization in ultrasound vascular imaging is carried outusing volume-based rendering. In achieving this end, oneof the most commonly used techniques in 3-D vascularimaging is based on the ray casting algorithms that pro-jects a 2-D array of rays through the 3-D image (58). Al-though 3-D vascular imaging is very promising inrevealing vascular structure and pathology, more work isneeded in the directions of fast and accurate free handscanning, automated or semiautomated segmentation,real-time and user friendly visualization, and 3-D textureanalysis (58). Advances in these directions will enable thewidespread use of 3-D imaging in clinical practise in theassessment of carotid atherosclerosis, as has been the casein cardiology and fetal imaging.

Rapid advances in information technology and telecom-munications, and more specifically, wireless and mobilecommunications, and their convergence (telematics) areleading to the emergence of a new type of information in-frastructure that has the potential of supporting an arrayof advanced services for health care. Telemedicine can bedefined as the delivery of health care and sharing of med-ical knowledge over a distance using telecommunicationmeans. It aims at providing expert-based medical care toany place that health care is needed. Likely, in ultrasound

imaging, the need exists for the assessment of vascularimages/video either by a second expert or a panel of ex-perts, making the capturing and transmission of digitalultrasound a necessity. In general, over the years, digitalultrasound has become more acceptable in clinical prac-tice (62–67). In considering the future use of digital ultra-sound, it is important to distinguish between ‘‘storage andforward’’ and video streaming applications. In ‘‘storageand forward’’ applications, the entire ultrasound video canbe stored and transmitted as a whole. Such applicationscan be handled using MPEG-2 (68). In ‘‘storage and for-ward’’ applications, it is assumed that sufficient time ex-ists to retransmit the video as needed. Instead, videostreaming applications require video decoding prior to re-ceipt of the entire video (69). Usually, a short delay existsbetween the time the video is transmitted and the time itis decoded. Most of the technology challenges are associ-ated with video streaming applications over noisy, wire-less channels. Error-control mechanisms must deal withboth single-bit errors as well as the loss of video packetsduring the transmission. In video streaming applications,it is not possible to retransmit video packets indefinitely,as the long delays associated with retransmission may beunacceptable. Thus, error-control mechanisms attempt toencode the video so as to enable recovery from packetlosses, and also to minimize the perceived error followingerror detection (69). The effect of error-control mecha-nisms on clinical diagnosis is still an open area of re-search.

In the context of the European Commission, DG XII,Biomedical and Health Research Program 1994–1998, theproject entitled ‘‘The Value of Non-Invasive Investigationsin Identification of Individuals with Asymptomatic Car-otid Stenosis at Risk of Stroke (ACSRS),’’ (January 1997–December 1999), which is also supported by the Interna-tional Union of Angiology, investigated the identificationof individuals with asymptomatic carotid stenosis at risk

Figure 10. Percentage of correct classificationsscore for the SOM classifiers for the texture featuresets 1: SD, 2: SGLDM mean values, 3: SGLDMrange of values, 4: GLDS, 5: NGTDM, 6: SFM, 7:TEM, 8: FDTA, 9: FPS, and 10: when combining theoutputs of SOM classifiers of feature sets 1 to 9.


of stroke. An integrated database system was developedtaking into consideration important stroke-related clinicalrisk factors, and noninvasive (paraclinical) parameters(i.e., high-resolution ultrasound images of the carotidand CT brain scans). This integration facilitates the datamining analysis for the assessment of the risk of stroke. Itis anticipated that the extraction of quantitative criteria,for the identification of high- and low-risk subgroups ofpatients, will be a decisive factor for the selection of thetherapy, either medical or surgical. Thus, only patients athigh risk will be considered for surgery (carotid endart-erectomy), whereas patients at low risk will be sparedfrom an unnecessary and expensive surgery that also car-ries a risk.

Linked with the integration of imaging and clinical da-tabases is the content-based access for the retrieval of im-ages and video. In the study of an ultrasound image of thecarotid artery, the atherosclerotic plaque could be seg-mented and used for searching for similar plaque mor-phology in the imaging database. The system would showthe similar plaques found in the database and displaythem together with the corresponding clinical findings.Hence, an assessment could be made based on the obser-vation of similar cases.

Atherosclerosis is a multifactorial disease that makesthe process of prevention and disease management highlycomplex. In addition to the many factors that are useful inassessing an individual’s risk of developing a cardiovas-cular event, recently, biochemical markers for cardiovas-cular disease have been identified such as homocysteine,C-reactive protein, and fibrinogen. However, further workin this area is needed in order to understand and identifytheir exact role in disease. High-resolution ultrasoundimaging offers the potential of determining phenotypesmore accurately than using conventional risk factors andclinical events, which is achieved because plaque echo-density can characterize the plaques that are unstableand likely to rapture (70). The ability to identify this typeof plaque and, hence, high-risk individuals also offers theadvantage of monitoring plaque stabilization drug thera-pies and the development of new therapeutic strategies.

It is hoped that once genes contributing to atheroscle-rosis have been identified, and the combination of DNA-based tests, risk factors and quantitative ultrasound vas-cular imaging facilitated via emerging medical image pro-cessing and instrumentation, will contribute toward theimplementation of the most effective strategy to minimizecardiovascular death and offer a better service to the cit-izen.

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