automatic segmentation of the lumen of the carotid artery in ...

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056Volume 2 Issue 7, October 2015 www.irjet.net p-ISSN: 2395-0072

© 2015, IRJET ISO 9001:2008 Certified Journal

AUTOMATIC SEGMENTATION OF THE LUMEN OF

THE CAROTID ARTERY IN ULTRASOUND B-MODE

IMAGES USING FUZZY LOGIC CLASSIFIER

D.SASIKALA1 B.NANDHINI2 M.NARGEESH BANU3

A.NIVETHA4 R.SOWMIYA5

1Professor/ECE , Vivekanandha College of Engineering for Women, Tamilnadu,India

2,3,4M.E VLSI design, Vivekanandha College of Engineering for Women,Tamilnadu,India

5M.E Applied Electronics, Vivekanandha College of Engineering for Women,Tamilnadu,India

Abstract:

In this paper, the new method isproposed for recognizing B-mode ultrasound(US) imaging of common carotid artery (CCA)in longitude. Automatic segmentation of thearterial lumen from ultrasound images is animportant task in clinical diagnosis. Carotidartery recognition, the first task in lumensegmentation, should be performed in a fullyautomated, fast, and reliable way to furtherfacilitate the low-level task of arterialdelineation. In this paper, a user-independent,real-time algorithm is introduced for carotidartery localization in longitudinal B-modeultrasound images. The proposed method hasfour major steps. The process starts withimage area selection, vertical intensity profile(VIP) signal selection, lumen center pointdetection; the proposed method classifiescenter points using fuzzy logic classifier. Thisprovides a medial axis with efficient lumenrecognition data. The data sets used included2,149 images from 100 subjects taken fromthree different institutions and covering awide range of possible lumen andsurrounding tissue representations. Using theoptimized values, the carotid artery wasrecognized in all the processed images in bothmulti-frame and single-frame data. Thus, theintroduced technique will further reinforce

automatic segmentation in longitudinal B-mode ultrasound images.

Index Terms—Lumen recognition, carotid artery,segmentation, ultrasound, fuzzy logic

I.INTRODUCTION

The common carotid artery (CCA) is theartery that supplies the human head, specifically thefront part of the brain and neck, with oxygenatedblood. Like other arteries, it is known for its pairedstructure: one for left part (with origin in the aorticarch) and another one for the right part of thehuman body (with origin in the neck). Vascularnon-invasive ultrasound (US) allows the estimationof morphological and dynamic parameters ofarteries, such as diameter and distension (Renemanet al., 2005) or intima-media thickness (IMT) (VanBortel et al., 2001). To perform thesemeasurements fast, accurately and reliably withminimal inter and intra-user variability, thesegmentation of the underlying ultrasound imageshould be computerized.

Image segmentation may be thought asconsisting of two related processes (Udupa et al.,2006): recognition, i.e. the high level task ofdetermining roughly where a specific structure islocated, and delineation, i.e. the low level task ofdetermining the precise spatial extent of suchstructure. The topic of this paper concerns CArecognition through lumen medial axis detection.



So far, the various methods are introducedfor CA segmentation requires manual userassistance in arterial lumen recognition (see [5]–[9]).few algorithms offer full automation in CAsegmentation (see [16],[2],[3]). The user-inducedartifacts were limited by completely automated CArecognition [1], thus providing (i) better accuracyin recognizing the random-shaped arterial lumenand (ii) repeatability, whereas it provides (iii)increased productivity compared to the manual one.Moreover, focusing on recognition alone, whichremains the most challenging task in imagesegmentation, may also (iv) improve overallsegmentation accuracy by facilitating themeasurement procedure [1], (v) reduce overallcomputational cost, (vi) provide better flexibility,and (vii) offer better error control, because it isevaluated independently.

II.RELATED WORK

To our knowledge, previous work focusedsolely on automatic arterial recognition inlongitudinal B-mode images is relatively limitedcompared to the work on CA delineation. Delsantoet al. [14] introduced an automatic region ofinterest (ROI) identification approach asinitialization for a user-independent segmentationalgorithm. However, the proposed algorithm maybe deceived by wall calcium plaque presence, andit is not well-suited for real-time applications.Rossi et al. [1] developed a real-time algorithm forthe automatic recognition of the common CarotidArtery (CCA) that acts directly on the envelopes ofreceived radio frequency echo signals.

However, their method can be exploited toits full extent when applied to multi-frame data.Wan et al. [15] suggested two different methods forCCA recognition; a single-frame, and a multi-frame approach. However, both their methods arealso prone to low Signal-to-Noise Ratio (SNR) inthe lumen and to jugular vein presence. Benes et al.[19] proposed an automatic CA localizationapproach based on a support vector machine(SVM) classifier and a novel random sampleconsensus (RANSAC) method [18] to suppressmisclassified points. However, their approach is not

appropriate for real-time applications. EmmanouilG. Sifakis et al. [4] proposed carotid arteryrecognition based on Vertical intensity profile(VIP)for medial axis formation in lumen. The drawbackof this method uses a basic classification for CArecognition.

The main aim of this paper was to developa fully automated and real time technique for CArecognition in longitudinal B-mode ultrasoundimages. The method operates efficiently on a singleultrasound image without the need of utilizing anysubsequent frame information. Our CARecognition approach based on a Fuzzy logicclassifier to suppress misclassified points. It wasalso systematically compared on an equal footingwith another, promising method for CCAlocalization.

III.MATERIALS AND METHODS

A. Image DatasetsIn this study, a total of 2,149 longitudinal

B-mode ultrasound images of the CA recordedfrom 100 subjects, and collected from threeseparate datasets namely St. Mary’s HospitalDataset (SMH), Aretaieion University HospitalDataset (AUH), SPLab Dataset (SPL) were usedfor performance evaluation of the proposedalgorithm. Fig. 1 shows an simple CCA and Fig 2shows longitudinal B-mode ultrasound imageexample of the tested ultrasound images. Allimages are depicting CCA in longitudinal scan of

different volunteers.

Fig 1. Diagram of common carotid artery wall



The images were scanned with different settingsof acquisition hardware (frequency, depth, gain)

and different positioning of a probe. The images

cover a wide spectrum of (i) CA sites (e.g.common, internal, external, bulb), (ii) subjectprofiles (e.g. age, sex, weight), and (iii) clinicalconditions, and they were acquired using different(iv) ultrasound image acquisition systems andlinear array transducers, (v) sonographers, and (vi)settings (e.g. depth, gain, time gain compensation(TGC)).

Fig 2. Longitudinal B-mode ultrasound image ofcommon carotid artery with layer description.The lumen is the region where the blood flows

B. Algorithm ArchitectureThe overview of the proposed algorithm,

which was developed and evaluated within Matlab® 7.9 R2009b (The MathWorks Inc., Natick, MA,USA), is shown in Fig. 3. Briefly, a finite numberof equally spaced, vertical intensity profiles (VIP)is considered in a single image. For every VIPsignal, a statistics-based procedure is used for asingle lumen center point identification. Then, asubset of the resulting lumen center points,designated as the ‘backbone’, is further processedto accurately estimate the CA lumen position.

1) Image Area SelectionIn US longitudinal scans, the CCA appears as astructure oriented in horizontal direction, oftencovering the entire frame width. In the verticaldimension of the US frame, the artery is usuallylocated at depths between 10 and 30 mm with adiameter between 4 and 9 mm, depending on ageand sex of the subject. The artery structure appears

as a region having very low echogenicity (thelumen) surrounded by two bright bands (the arterialwalls).The lumen center position may vary over theframe, consistent with a curved or inclined artery orwith local narrowing’s of the lumen due to astenosis.

A vascular US image may contain manypatterns mimicking the appearance of an artery of

interest, e.g. actual tissue structures, artifacts,reverberations and other vessels (Fig. 3).The upper

Fig 3. Ultrasound B-mode image of the commoncarotid artery (CCA).part of each frame (the first 2–3 mm) containsparticulars that are of no interest for the lumenlocalization task (the gel skin interface forinstance). Moreover, users often tend to have theregion of interest in the center of the image.Therefore, we ignore the information contained inthe upper 3 mm and in the lateral 4 mm on eachside (10% of the image width).whereas preservingthe entire image height.

2) VIP Signal SelectionIn the reduced image, the concept of

lateral interspacing adapted by Rossi et al. [1] forvertical signal selection [3], [1], [14] was followed.Specifically, a finite number (N) of VIP signals wasconsidered every Sstep mm (Fig. 4(b)), thus reducingits 2D information content to a series of 1D signals.The lateral interspacing of Sstep=0.5 mm wasselected, because it provides a rather adequatesample size for robust and accurate CArecognition, and has a step relatively low



1. IMAGE AREASELECTION

2. VIP SIGNALSELECTION

3. SINGLE LUMENCENTER POINT

DETECTION

A) VIP LOCAL MEAN AND VARIANCE COMPUTATION

B) LUMEN AND WALL SEGMENTS IDENTIFICATION

C) SEGMENT FILTERING

D) LUMEN CENTER POINT DETECTION(FUZZY LOGIC CLASSIFICATION)

STEP 3 REPEATFOR EVERY VIP

SIGNAL

4. BACKBONEPROCESSING

D) BACKBONE INITIALISATION

C) BACKBONE FILTERING

B) BACKBONE EXTENSION

A) BACKBONE SMOOTHING

FINAL OUTPUT IMAGE OFARTERY WALL RECOGNITION

Fig 4 Block diagram of proposed system



Fig 5. VIP signal selection with swl=0.5,arrowindicates 10th VIP signal from left.

computational cost. After testing, anyvalue of sstep in the interval (0 0.5] mm yielded thesame success rate, while smaller values were onlyat the expense of increased computational cost.

3) Single Lumen Center Point Detection:For each selected VIP signal (Fig. 4(b)), a

statistics-based, multistep procedure was used forthe estimation of a single lumen center point. TheCA anatomical characteristics were used for this itsprocess from the bottom of the image and movingupwards, first one ordinarily encounters theprocedure. In particular, the ultrasound image starts(usally brightest) far wall region, then the (usuallydarkest) lumen, and then the (usually secondbrightest) near wall.

The proposed procedure consists of thefollowing sequential steps:

3.1 VIP local mean and variance computationDuring this step, a sliding window of fixed

length swl is used to calculate the smoothed localmean and variance of the VIP signal. The selectionofswl is a result of a trade-off between including arelatively large number of intensity points toimprove the estimated statistics, and following theintensity-dependent ‘steepness’ observed close tothe lumen borders more effectively. The length ofswl = 0.5 mm was initially selected, equal to half ofthe mean adventitia thickness [18], i.e. accountingalso for extreme cases where only the adventitiaappears bright in the ultrasound image, while theother layers appear dark.

3.2 Potential lumen and wall segmentsidentification

This step is followed to identify ROIsbelonging to the vessel lumen, and ROIs belongingto the vessel wall. This approach divides thedistributions of the local mean and variance verticalintensities using percentile-based thresholds torecognize the potential corresponding ROIs. Thus,because vessel lumen pixels typically have lowmean intensities and low variances. The VIPsignal’s potential vessel lumen segments areselected only if local mean intensity (LLoc = lower25th percentile), and, at the same time, (ii) localvariance intensity distribution (Ldisp = lower 25th

percentile).Similarly, because vessel wall pixels

should have relatively high mean intensities, a VIPsignal’s segment is considered as possiblybelonging to the vessel wall if its correspondinglocal mean intensity distribution belongs to itsupper quartile (W loc = 75th percentile).

Fig. 6(a) shows the potential lumen and wallsegments superimposed on the original ultrasoundimage.3.3 Segment filtering

The identified lumen and wall segmentsproceeds with two cascade routines of filtering.The first one fills the small gaps between twoconsecutive segments. The second one discardsshort length segments. Thus, at the beginning, thedistance between two consecutive segments iscalculated and if it is found lowers than a user-defined distance threshold, the two segments aremerged into a single segment. Subsequently,segments shorter than a user-specified threshold arerejected.

Fig 6(a) lumen and wall segments identified output(b) Segment filtered image

T



The four cut-offs for the lumen and wallsegments, namely the two distance thresholds(Lfillgap and Wfillgap) and the two length thresholds(L minlength and Wfillgap), are selected considering (i)that the mean adventitial thickness is about 1 mm[18], and (ii) two worse-case scenarios. Theadventitia (of either the near, or the far wall)appears bright in the ultrasound image, while theother layers of the vessel wall appear dark; appeardark; that is, they cannot be discriminated frombackground (e.g. blood).

In this extreme case, two consecutivesegments possibly belonging to the vessel lumenshould be merged only if their distance is notgreater than the adventitial thickness. Thus, theminimum distance threshold for the lumen

segments Lfillgap should be equal to 1 mm. potential

wall segments should have minimum length equalto the adventitial thickness; thus, the lengththreshold for the wall segments Wminlength was alsoset equal to 1 mm. In the other worse-case scenario,the vessel lumen is heavily occluded such that thelumen diameter locally becomes very small.Defining this minimum lumen diameter as 1 mm,the two remaining cut-offs may be set in a similarmanner. Specifically, two consecutive segmentspossibly belonging to the vessel wall should bemerged only if their distance is not greater than thelumen diameter. Thus, the minimum distancethreshold for the wall segments Wfillgap should beequal to 1 mm. Furthermore, the potential lumensegments should have minimum length equal to theminimum lumen diameter; thus, the lengththreshold for the lumen segments Lminlength was alsoset equal to 1 mm. Accordingly,Wminlength = Lfillgap

and Lminlength = Wfillgap.Fig. 6(b) illustrates the filtered potential

lumen and wall segments superimposed on theoriginal ultrasound image.3.4 Potential lumen center point identification

To automatically identify the VIP signal’spotential lumen center point, a heuristic search offuzzy logic classifier [23] is applied to the filteredlumen and wall segments. In particular, the VIPsignal is searched for (i) the first wall-lumensegment pair starting from the bottom of the image(Group U), or, instead, for (ii) the last wall-lumensegment pair starts from the top of the image(Group D).To facilitate the classifier, the midpoint

of each line segment is taken as its representative.Thus, at the end of the procedure the output wouldbe a single potential lumen center point for eachVIP signal. Each point is resulted either fromGroup U or Group D (or none of them), and thus itis designated accordingly.

Fig.7(a) shows the output of this step for alow-quality ultrasound image.

Fig 7(a) Lumen center point detection output,where a potential point is selected for eachcorresponding VIP signal. Each point belongseither to Group U (upward pointing triangles),or to Group D (downward pointing triangles).The backbone is initialized as the subset ofGroup U points (appearing as white-filled,upward-pointing triangles). (b) Backbonefiltering output. (c) Backbone extension output(circles indicate the filtered, while dots theextended points). (d) Backbone smoothingoutput.

Fuzzy ClassifierThe classification was performed on the

basis of image local features obtained from aneighborhood of particular pixels. The appropriateselection of features is crucial for the performanceof the classifier.[23] It is important to selectfeatures that separate both classes. In thisimplementation, appropriate features were selectedaccording to our local mean and variance of VIPsignal.4) Backbone Processing

At the end of the application of the lumencenter point detection algorithm to all VIP signals,



a number of Group U and/or Group D points thatequals or is smaller than N is generated.[4]However, due either to artifacts in some VIPsignals, or to the impossibility to findsegments/points meeting the imposed constraints,the resulting set of points needs to be furtherprocessed to substantially increase the success rate.Therefore, another multi-step procedure isfollowed, the input of which consists of a subset ofthese points. This subset of points, designated asthe ‘backbone’, is suitably initialized, filtered,extended and smoothed, so as to extract a robustand accurate arterial lumen center representation.More specifically:

4.1 Backbone initializationBecause the far wall region appears

typically brighter than the near wall [21], pointsbelonging to Group U are considered with higherconfidence than those belonging to Group D.Hence, the backbone is initialized as the Group Upoints. However, Group D points are alsopreserved for potential usage in the backboneextension step described below.

4.2 Backbone filteringThe initial backbone needs to be filtered

for outliers. However, this is an extremelychallenging task, due to the fact that the CA can beslightly inclined and/or curved within the image,which implies variations in the lumen centerposition over the image. These variations, whichcorrespond to true lumen points, cannot be easilydiscriminated from outliers.

To overcome this, a two-stage procedureis introduced that comprises an adaptive filteringthat becomes more severe in more ambiguous cases(i.e. in cases where backbone points are verydisperse) gradually. The goal of the proposedapproach is to output a filtered backbone thatconsists solely of true lumen estimates. Apparently,this also comes at the expense of discarding a fewor a relatively larger number of true lumen centerpoints, too (in particular, the more disperse thebackbone points are, i.e. the more inclined and/orcurved the lumen is, the more true backbone pointswill be eliminated). However, to compensate forthis effect, a heuristic backbone expansionalgorithm is applied afterwards (see, the followingstep).

In ultrasound longitudinal images, it isquite common to encounter CA-mimickingpatterns, which (i) appear in a different depthrange, and (ii) are usually more localized, spanninga smaller part of the image width (Fig. 4(a)).Therefore, most of the CA center points aredistributed around a specific depth level, which isalso different from that of all other vessel-likestructures’ center points.

Based on the hypothesis that the majorityof the true backbone points is located in a similardepth within the ultrasound image [1], the two-stage filtering algorithm considers the distributionof the initial backbone’s y-coordinates (depthvalues), along with its initial mean and standarddeviation (SD). At the first stage, points with y-values greater than f SD0 = 1.5 SD from the mean (acommon selection for filtering outliers in statistics)are identified and excluded.

If the initial backbone points are highlydisperse, i.e. the initial SD exceeds a maximumacceptance threshold, the process is repeated untilconvergence. At the second stage, the newdistribution of points is calculated, along with itsnew mean and SD. In the case where the new SDexceeds a maximum acceptance threshold, i.e. thefiltered points are still highly disperse, the firststage is repeated until convergence, however, thistime a new SD is computed and the initial thresholdof fSD0 = 1.5 SD is reduced by f SDstep = 0.1 SD atthe end of every cycle (any value of f SDstep in therange [0.05 0.5] does not significantly affect thealgorithm’s final success rate). The second processis repeated until SD does not exceed the maximumacceptance threshold anymore. Thus, the morescattered the new point distribution, the moresevere the filtering.

To select an appropriate value for themaximum acceptance threshold used in bothfiltering stages, the probability density function(pdf) of SD was estimated. Particularly, the pdf ofthe initial backbone points’ SD (initial SD) of ~103

ultrasound images was estimated using a Gaussiankernel density estimator [11]. The image set usedfor the pdf estimation of SD included images fromall the datasets to ensure a wide range of possibleSD values. Specifically, we included all the imagesfrom datasets AUH and SPL, and 10 recordingsfrom dataset SMH (5 healthy young, 2 healthyelderly, and 3 stenotic subjects).



Fig. 8. Probability density function of SD of (a)the initial backbone (light gray, dotted line), (b)the output backbone at the end of filtering stage1 (dark gray, dash-dotted line), and (c) theoutput backbone at the end of filtering stage 2(black, solid line), resulting from a non-parametric kernel density estimation, and usinga maximum acceptance threshold of 1.

As illustrated in Fig. 8(a), the probabilityof the initial SD to fall within region (0, 1) isrelatively higher than to take on values that exceedthe upper limit of that range. The higher SD valuescorrespond to more dispersed backbone pointdistributions that may result either from a relativelylarge number of false-positive points, or from ahighly inclined and/or curved CA. Therefore, thevalue of f maxSD = 1mm was chosen for themaximum acceptance threshold (any value smallerthan the selected one does not significantly alter thealgorithm’s final success rate). Fig. 8(b), (c) showthe SD’s pdf estimates at the end of filtering stages1 and 2, respectively.

The filtered backbone of the low-qualityimage is shown in Fig. 7(b).4.3 Backbone extension

During this step, a heuristic extensionalgorithm is applied with the purpose of either (i)correcting/tuning the backbone initialization step,thus accounting for cases where part(s) of theinitial backbone is missing (resulting from e.g. low-quality images with far wall part(s) appearing faint,as in Fig. 7(a)), or (ii) expanding the severelyfiltered backbone, thus accounting for a potentialtrue backbone point(s) elimination (resulting fromambiguous cases, e.g. due to high lumen inclinationand/or presence of CA mimicking structure(s)).

Fig.9. Example of the backgroundextension algorithm concept. The circlescorrespond to a filtered backbone left end, whilethe horizontal line to its local depth level. Thearrow indicates the VIP position where thediscontinuity begins, thus the search initiationfor potentially filtered out backbone points. Forthis first VIP position, a candidate point shouldbe located within the range indicated by thevertical line, i.e. should be maximum up to Te1

mm distant from the local depth level. If this isnot the case, the search moves to the next VIPposition, and so on (until a maximum number of20% of N), each time using an exponentiallydecreasing threshold Te. The vertical lines,formulating a discrete funnel-like area,represent this new point’s search range. In thisexample, the new point is located within therange of the first VIP position (indicated by thesquare), thus it is included in the backboneextension set. The same procedure is initiatedfor all the neighboring VIP positions, until nomore new points enter the backbone.

More specifically, given the existence ofat least one discontinuity (void) in the output of thebackbone filtering procedure (created after thebackbone initialization and/or filtering steps, as inFig. 7(b)), at the left and/or the right of everyfiltered backbone end, the superset of thecorresponding Group U and Group D points issearched for a potential neighboring lumen centerpoint. To specify this ‘adjacency’, firstly, acorresponding small window of fixed length xwl(equals 10% of N) is applied at the vicinity of thebackbone end, and a local depth level value isdetermined. The latter is defined as the mean of thedepth values of all the filtered backbone pointslocated within the specified window. Then, thepotential neighboring point is included in theextended backbone, only if its distance from thelocal depth level is smaller than a step-wisely



decreasing threshold Te, which is initialized withthe maximum value of 2 mm.

In particular, if the candidate lumen pointis not located within the initial value of Te1 = 2 mmfrom the local depth level (in the y direction), thealgorithm searches for the next candidate point atthe next VIP position, and so on (within an intervalthat equals 20% of N in the x direction), each timeusing the exponentially decreased value of Te( ( ) = . λ (n − 1),n= 1,….,~ 0.2N) and adecay constant of λ = 5 (even though parametersxwl and Te should be kept relatively small andparameter λ relatively large, the algorithm’s finalsuccess rate is not significantly affected over abroad range of their corresponding values).

An exponential decrease with a relativelylarge decay constant was preferred over e.g. alinear decrease in order to be more stringent, andhence minimize the potential of false positivepoints inclusion. Thus, a discrete funnel-like searcharea is formulated within which a potentialbackbone extension point should be located (Fig.9). The procedure is repeated for each ‘missing’backbone point, calculating an updated local depthlevel each time a new point enters the backbone.The backbone extension algorithm output isillustrated in Fig. 7(c).

4.4 Backbone smoothing:As a final step, the extended backbone is

further smoothed to account for potential spikepoints, emanating e.g. due to speckle contentand/or localized artifacts. To smooth the backbonewithout altering its local traits, the locally weightedleast squares regression loess method was applied,using a 1st degree polynomial model and asmoothing window mwl equal to 20%. Any valuefor mwl in the range [10 30] % could be used.However, it is recommended not very largevalues (e.g. mwl 40%) to be used in order not toalter the natural curvature of the identifiedbackbone.

The final result of the automated CArecognition algorithm is illustrated in Fig. 7(d).

IV RESULTS

To validate our algorithm, as well as tofine tune some of its preselected parameters, aprocedure based on 3-fold cross-validation wasperformed. Specifically, completely independent

image sets were used for (i) parameter tuning, and(ii) testing and evaluating the proposed method.Moreover, instead of segmenting the originaldataset, another initial instead of segmenting theoriginal dataset, another initial image set wasformed that contained the complete AUH and SPLdatasets, and only the first frame of each recordingof the SMH dataset, in order to eliminate anypotential favorable bias during the different subsetformation. This dataset was first partitioned intothree nearly equally sized subsets. Subsequently,three iterations of parameter tuning and evaluation

Fig. 10. Demonstration of the robust nature ofthe proposed CA recognition algorithm (a)-(d)in very low-quality B-mode images from theSMH dataset and the AUH dataset.

were performed such that within eachiteration a different subset of the data was held-outfor algorithm’s testing and evaluation (testing set),while the remaining two subsets were used foralgorithm parameter tuning (training set).

In each task (parameter tuning andevaluation), the automatic CA recognition outputswere compared with semiautomatic lumen centerdelineations made by an expert sonographer.Specifically, for an image, the user manuallyidentified (i) the intima-lumen interface of the near



wall and (ii) the lumen-intima interface of the farwall, from where the reference arterial centerlinewas computed.

The semi-automatic delineation wasperformed on each image for the AUH and SPLdatasets, and on the first image of each sequencefor the SMH dataset. For the rest of the images inthe sequence, the two aforementioned interfaceswere automatically tracked using the adaptiveblock matching technique ABM_FIRF2, describedin detail in [22]. The algorithm’s automatic CArecognition output was considered correct if it didnot deviate more than 2 mm from the referencetracing [3], [1].

To rate the results of the automatic CArecognition, three performance metrics wereconsidered, namely (i) the success rate SR (%),which indicates the success percentage in the totalnumber of images in the set, (ii) the mean framecoverage FC(%), which is expressed by the ratio ofthe distance between the outermost VIP signalswith a correctly identified centerline and the resizedimage width [1], and (iii) the mean distance D(mm) between the automatic and the referencecenterline position. The mean FC and D valueswere derived after averaging over all the processedimages.

The algorithmic parameters that wereselected from tuning results shown in [4].Theoptimized values of the tuned algorithm’sparameters overall are (swl, Ldisp , Lloc , Wloc )= (0.5mm, 25th,50th,75th).

Fig. 10 shows some automatic lumenrecognition examples using the above optimizedparameter values.

V. DISCUSSION

In this study, a novel algorithm wasintroduced for the automatic recognition of the CAin longitudinal B-mode ultrasound images. Theproposed algorithm consists of multiple cascadedsteps with fuzzy logic classifier and exploits basicstatistics along with arterial anatomical knowledge.The validity of the technique was investigated bydirect comparison with expert’s results over acompletely diverse dataset range on a 3-fold crossvalidation basis. Through this process (trainingphase), a fixed optimized parameter set was

derived, capable of successfully localizing CA withfull automation in a wide range of possible arterialrepresentations.

The developed method performed veryeffectively and successfully localizes the CA basedon a low computational complexity algorithm,whereas operating directly on the raw image, e.g.without the need of any time consuming 2D imageprocessing techniques. wide range of parametervalue combinations.

Furthermore, the examples of Fig. 10illustrate the algorithm’s effectiveness using thesame optimized thresholds in images (i) of poorquality (ii) with atherosclerotic plaques, (iii) withCA-mimickingstructures appearing above and/or below the CA,(iv) with relatively high and/or varying luminalgray level, and (v) with different arterialinclinations and/or curvatures. All of the aboveindicate the high performance and high robustnessof the introduced approach and its toleranceparameterization.

The algorithm was designed based mainlyon the fact that the arterial lumen has (i) lowermean intensity and lower variance (apart from theadventitia layer) compared to its surroundings, and(ii) a smoothly curved shape. In some images, arelatively low FC was observed, probably due to acombination of more than two of the followingfactors: (i) a high (and/or variant) luminal intensity,(ii) CA mimicking structure presence covering asignificant part of the entire image width(especially below the CA), (iii) a poor far wallrepresentation, and (iv) an abruptly curved arterialshape (e.g. due to a relatively large plaque). Inthese cases, a certain percentage of backbonepoints was missing (discarded during the backbonefiltering step), resulting in a shorter final backbone(e.g. Fig. 10(a)).

Furthermore, in cases of non-uniform luminalintensity, caused e.g. by high speckle contentand/or artifacts residing within the arterial lumen,the backbone appeared slightly affected. Also, thealgorithm may not be able to distinguish betweenlumen and hypo echoic tissue, e.g. when increasedspeckle content and/or a highly stenotic hypoechoic plaque are present, which is generallybelieved to be associated with soft and unstabletissue. These limitations, however, are consideredof minor significance, in the context of this study,



given that the purpose of the suggestedmethodology was the unambiguous identificationof a ROI that includes the CA [1]; objective whichis fulfilled.

VI CONCLUSION

In summary, the algorithm described inthis paper provides a simple, fast, robust and highlyefficient way to locate the CA in longitudinal B-mode ultrasound images. Applied directly to theraw image, it managed to recognize the CA withhigh robustness and reliability. Moreover, themethod’s particularly low computational cost, itssingle frame sufficiency, along with itsextendibility, facilitates both a wider range of on-line and off-line applications. All these render theintroduced technique a suitable and reliable tool forlumen recognition that will further reinforce thecompletely user independent segmentation of theCA in longitudinal B-mode ultrasound images.

ACKNOWLEDGEMENT

This work is supported by St. Mary’sHospital, London, UK, for the recording of theSMH dataset and also for the University of Athens,Aretaieion Hospital for helping in the AUH datacollection.

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