06547196

3194 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 11, NOVEMBER 2013

An Automated Method for Retinal ArteriovenousNicking Quantification From Color Fundus Images

Uyen T. V. Nguyen, Alauddin Bhuiyan, Laurence A. F. Park, Ryo Kawasaki, Tien Y. Wong, Jie Jin Wang,Paul Mitchell, and Kotagiri Ramamohanarao∗

Abstract—Retinal arteriovenous (AV) nicking is one of theprominent and significant microvascular abnormalities. It is char-acterized by the decrease in the venular caliber at both sides ofan artery-vein crossing. Recent research suggests that retinal AVnicking is a strong predictor of eye diseases such as branch retinalvein occlusion and cardiovascular diseases such as stroke. In thisstudy, we present a novel method for objective and quantitative AVnicking assessment. From the input retinal image, the vascular net-work is first extracted using the multiscale line detection method.The crossover point detection method is then performed to local-ize all AV crossing locations. At each detected crossover point, thefour vessel segments, two associated with the artery and two as-sociated with the vein, are identified and two venular segmentsare then recognized through the artery-vein classification method.The vessel widths along the two venular segments are measuredand analyzed to compute the AV nicking severity of that crossover.The proposed method was validated on 47 high-resolution retinalimages obtained from two population-based studies. The experi-mental results indicate a strong correlation between the computedAV nicking values and the expert grading with a Spearman corre-lation coefficient of 0.70. Sensitivity was 77% and specificity was92% (Kappa κ = 0.70) when comparing AV nicking detected us-ing the proposed method to that detected using a manual gradingmethod, performed by trained photographic graders.

Index Terms—Arteriovenous (AV) nicking, blood vessel seg-mentation, crossover point detection, retinal image, vessel widthmeasurement.

Manuscript received March 7, 2013; revised May 20, 2013; accepted June16, 2013. Date of publication June 25, 2013; date of current version October16, 2013. Asterisk indicates corresponding author.

U. T. V. Nguyen is with the Department of Computing and Information Sys-tems, The University of Melbourne, Parkville, VIC 3010, Australia (e-mail:[email protected]).

A. Bhuiyan is with the ICT Centre, Australian E-Health Research Centre,Commonwealth Scientific and Industrial Research Organization (CSIRO), PerthWA 6014, Australia (e-mail: [email protected]).

L. A. F. Park is with the School of Computing, Engineering and Math-ematics, The University of Western Sydney, NSW 2751, Australia (e-mail:[email protected]).

R. Kawasaki is with the Department of Public Health, Yamagata Univer-sity Faculty of Medicine, Yamagata 990-9585, Japan (e-mail: [email protected]).

T. Y. Wong is with the Singapore Eye Research Institute, National Universityof Singapore, Singapore (e-mail: [email protected]).

J. J. Wang and P. Mitchell are with the Centre for Vision Research, Depart-ment of Ophthalmology, Westmead Millennium Institute, University of Syd-ney (C24), Sydney, NSW 2006, Australia (e-mail: [email protected];[email protected]).

∗K. Ramamohanarao is with the Department of Computing and InformationSystems, The University of Melbourne,Parkville, VIC 3010, Australia (e-mail:[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TBME.2013.2271035

Fig. 1. Examples of AV crossings: (a) normal AV crossing and (b) AV nickingcrossing. (Please refer to the color version of the paper for a clear view of theimages.)

I. INTRODUCTION

R ETINAL arteriovenous nicking (AV nicking or AVN) isthe phenomenon where the vein is compressed by a stiff

artery at their crossing location in response to a rise in bloodpressure (i.e., hypertension) [1], [2]. In retinal photographs, AVnicking exhibits itself as the decrease in the venular caliber atboth sides of an artery-vein (AV) crossing (see Fig. 1). Theprevalence of moderate to severe AV nicking is reported as2.2% in the Beaver Dam Eye population [3] while this numberis found higher (7.7%) in the older population of the Cardio-vascular Health Study [4]. It was claimed that AV nicking isassociated with not only current blood pressure but also pastblood pressure, implying that it is a persistent and long-termmarker of hypertension [2]. Strong and consistent associationbetween AV nicking and systemic diseases has also been foundby recent population-based studies [5]–[13]. The Atheroscle-rosis Risk in Communities (ARIC) study reported that peoplewith AV nicking are two times more likely to develop an inci-dent stroke than those without (relative risk 2.21, 95% confi-dence interval [CI] 1.44–3.38) [6]. In addition, AV nicking wasalso found strongly associated to retinal vein occlusion, a com-mon sight-threatening ocular disorder (odds ratio 16.75, 95%CI 7.33–38.24) [14]. Therefore, the assessment of AV nickingis extremely important in order to identify people at high risk ofcardiovascular diseases for early and in-time treatment.

Currently, the assessment of AV nicking is done by humangraders in a subjective and qualitative manner. In this process,a grader examines all AV crossing points in a retinal imageand compares each of them with standard photographs to assessthe presence and severity of AV nicking for that image [15].This grading process, however, depends heavily on the graders’expertise and, thus, its accuracy and reproducibility are of con-cerns. The ARIC study reported that only a fair to moderate

0018-9294 © 2013 IEEE

NGUYEN et al.: AN AUTOMATED METHOD FOR RETINAL ARTERIOVENOUS NICKING QUANTIFICATION FROM COLOR FUNDUS IMAGES 3195

agreement is achieved with the manual AV nicking assessment(inter- and intragrader Kappa value κ = 0.40 to 0.61 [15] andκ = 0.56 to 0.57 in a more dedicated study [16]). In addition,the current manual grading process is very time consuming,which makes it infeasible for large-scale screening and clinicalapplications. In [17], we have proposed a computerized methodfor AV nicking measurement which helps to reduce the gradingtime substantially. However, this method also requires humanintervention as the user needs to select the crossover positionsin each retinal image. Motivated by this, we propose in thispaper a fully automated method, which is to the best of ourknowledge, the first computerized method proposed for auto-mated AV nicking assessment. The proposed method producesa continuous value, implying the AV nicking severity level, foreach AV crossing point in a retinal image. The advantages ofthe proposed method include:

1) The measurements produced are objective, reproducible,and repeatable.

2) The computed measurements reveal more details on AVnicking severity, which are not available in the humangrading system. Such information may help to strengthenthe relationship between AV nicking and known diseasessuch as hypertension and stroke.

3) The computed measurements provide an important basistoward the development of a computer system for au-tomatic AV nicking detection for large-scale screeningsystems.

The rest of this paper is organized as follows. In Section II, thedetails of the proposed method are described. The experimentalresults to demonstrate the performance of the proposed methodare presented in Section III. Finally, the paper is concluded withSection IV.

II. METHODOLOGY

The overall framework of the proposed AV nicking quantifi-cation method is illustrated in Fig. 2. The system takes as inputthe retinal image and returns a real number quantifying the sever-ity level for each AV crossing point detected from that image.From the input retinal image, the vessel segmentation method isapplied to extract the blood vessels from the image backgroundfor further analysis. A crossover point detection method is thenperformed to detect AV crossing locations within the retinal im-age. At each detected crossing position, the four major vesselsegments constituting the two vessels (i.e., the artery and thevein) are localized and the two venular segments are identifiedthrough an artery-vein classification process. The vessel widthsof each venular segment are then measured and analyzed forAV nicking measurement. The following sections describe eachstep of the system in details.

A. Vessel Segmentation

Blood vessel segmentation from high-resolution retinal colorimages is complicated by retinal vessel central light reflex,choroidal vascularization, and image artifacts such as back-ground homogenization and other impulse noises. Althoughmany methods have been proposed for retinal vessel extraction,

they are not effective for detecting blood vessels in our imageset that includes high-resolution images with the presence ofvessel central light reflex (i.e., the bright strip along the middleof a vessel). Motivated by this, we have proposed a new methodfor effective retinal vascular network extraction using multi-scale line detection technique, which is described in [18]. Theproposed segmentation method combines line detectors at vary-ing scales to produce an enhanced retinal image and the finalsegmentation is obtained by a thresholding operator. The pro-posed method was proven to be effective in dealing with centrallight reflex problem while providing accurate vessel boundarydetection.

Fig. 3 shows a cropped retinal image with the presence ofcentral light reflex and the segmented image obtained using ourmethod. It can be seen that the proposed method can correctlysegment all blood vessels, even on vessels with the presenceof central light reflex. Hence, it was used in our system forautomatic blood vessel extraction.

B. Artery-Vein Crossover Point Detection

The vascular network is constructed of three important land-mark points: branching, bifurcation, and crossover. A crossoveris the place where two vessels (i.e., a vein and an artery) crosseach other while a branching or a bifurcation is the place whereone vessel splits into two vessels. Since AV nicking happensat the AV crossing locations, the method first needs to detectall crossover points within a retinal image. For this task, anefficient method for automated crossover point detection hasbeen proposed and described in details in [19]. This section pro-vides a brief summary of the proposed method for the sake ofcompleteness.

To detect AV crossover points, the vascular skeleton and edgeimages are first extracted from the segmented image using bi-nary morphological operators. The vessel skeleton is extractedusing an iterative thinning process [20] which successivelyerodes away pixels on the boundary of the objects while pre-serving its connectivity until no more thinning is possible. Incontrast, the vessel edge image is achieved by a morphologicaloperator that removes interior pixels and retains only pixels onthe vessel boundaries [20]. The crossover locations are then de-tected by an extensive analysis on the extracted vascular skeletonand edge images as follows.

At each skeleton pixel P , we compute the cross-point number(cpn) [21] as follows:

cpn(P ) =12

8∑

i=1

|Ni(P ) − Ni+1(P )| (1)

where Ni(P ) are the neighbor pixels of P (in a 3 × 3 neighbor-hood) named in an anticlockwise order and N9(P ) = N1(P ).The cpn number computed for each skeleton pixel representsthe number of vessel segments connected to that pixel in theskeleton image. Hence, all skeleton pixels with cpn equal to 3are initially marked as bifurcation candidates while those withcpn equal to 4 are labeled as crossover candidates. Then, two bi-furcation candidates are further grouped as one crossover if theyare connected by at most T pixels. The segment that connects


Fig. 2. Main steps of the proposed method for automated AV nicking quantification.

Fig. 3. Example showing the vessel segmentation result: (a) cropped retinalimage with the presence of center light reflex (pointed by black arrows) and(b) segmented image.

Fig. 4. Geometrical features of two vessels constituting a crossover: (a) α1 andα2 represent intersection angles formed by the two vessels; (b) representationof α1 , α2 in the skeleton image; and (c) β1 and β2 represent the curvature ofeach vessel at the crossing.

the two bifurcation points is then identified and its middle pointis located and served as the true position of that crossover. Thisstep helps to detect crossover points that are represented by twobifurcation points in the skeleton image when two vessels inter-sect at an acute angle. However, this process also falsely detectsthose points that have the same configuration in the skeleton im-age as crossovers. To distinguish true crossovers from spuriousresults, two geometrical features of each detected crossover areanalyzed:

1) The intersection angles formed by the two vessels at thecrossing (α1 and α2 in Fig. 4(a) and (b)).

2) The two angles representing the curvature of each vesselat the crossing (β1 and β2 in Fig. 4(c)).

A detected crossover is valid if it satisfies:

(max(α1 , α2) < αmax) and (min(β1 , β2) > βmin) (2)

where αmax and βmin are two parameters to be set. We usedfive retinal images for parameter tuning and the experimen-tal results show that with our image set (see Section III-A),{T = 75, αmax = 110, βmin = 140} is a suitable setting. Theperformance of the proposed method was assessed on a subset

Fig. 5. Performance of the proposed crossover point detection method in termsof precision, recall, and F1-measure on 15 test images when T changes from 0to 100 pixels.

of 15 test images randomly selected from our image set (differ-ent from five training images). The results show that out of 41crossover points detected, 38 of them are correct, which indi-cates an accuracy (or precision) of 93%. In addition, our methodhas missed four crossovers, which means that the recall of ourmethod is 90%. This has demonstrated the effectiveness of ourmethod for crossover point detection. The detected crossing lo-cations are then fed directly to the AV nicking quantificationmethod so that their AV nicking severity can be assessed.

It should be noted that among the three parameters (T, αmax ,and βmin ), T is the only parameter that is dependent on the imageset (i.e., image resolution). Hence, the same values of αmaxand βmin can be used for a new image set. To investigate thedependence of the system on T , we examined the performanceachieved in terms of precision, recall, and F1-measure (F1 =2×precision×recallprecision+recall ) on 15 test images when T changes from 0 to

100 pixels (see Fig. 5). This figure shows that similar results areobtained for a long range values of T (65 < T < 100), whichimplies that the performance of the proposed method is not verysensitive to the setting of T .


C. AV Nicking Quantification

At each detected crossover, a subimage centered on the cross-ing location is identified and used for all subsequent analysisfor computational efficiency. Since AV nicking is characterizedby the decrease in the venular width at the crossing location, thetwo vessel segments constituting the vein are extracted and theirvessel widths are measured and analyzed for AV nicking com-putation. To achieve this, the segmented image at each crossoveris applied to a sequence of the following steps:

1) Major vessel identification2) Venular segment identification3) Vessel width measurement4) AV nicking measurementThe details of each step are described in the following

sections:1) Major Vessel Identification: This step aims to identify the

main vessels constituting the crossover and isolate them fromunnecessary structures such as small branches or noisy artifacts.To achieve this, the system first examines both vessel skeletonand edge images to detect and classify all feature points ascrossover (C), bifurcation (Bi) or branching point (Br). We havedescribed the method for crossover point detection in Section II-B. Branching point is the place where one small vessel comesout from a main vessel while a bifurcation point is the placewhere one vessel splits into two similar vessels. To distinguishbranching point from the bifurcation point, the vessel widths ofthe three vessel segments associated with that point are mea-sured and recorded as Wc (the vessel width of the trunk vessel),Wa (the vessel width of the smaller branch), and Wb (the vesselwidth of the larger branch). A feature point is classified as abifurcation if Wa

Wb≥ fb (0 < fb < 1) and as a branching point

otherwise. For example, fb = 0.5 means that at a branching lo-cation, if the vessel width of the smaller branch is equal to orgreater than half of the vessel width of the bigger branch, thatfeature point is considered as a bifurcation. This setting guaran-tees that two branches of a bifurcation are of similar diameter.

To identify a suitable setting for fb , we randomly selected tenbifurcation and ten branching points in the working image setand examined fb values (fb = Wa

W b ) of these selected landmarkpoints. For this, at each selected landmark point, we manuallymeasured the vessel width of its smaller branch (Wa ) and thebigger branch (Wb ). fb is then computed as the ratio of Wa

Wb.

Fig. 6 shows the distribution of fb values of these 20 landmarkpoints and it is shown that there is a clear separation in fb valuesof bifurcation and branching points: all bifurcation points havefb ≥ 0.8 while all branching points have fb ≤ 0.73. Hence, fb isset as 0.8 in our system for bifurcation/branching classification.

The process of major vessel identification is then performed asfollows. From the extracted vascular network, the system tracesfrom the crossing position through its four connected vesselsegments and performs appropriate action when a branching, acrossover, or a bifurcation is encountered. If a crossover or a bi-furcation is found, the system stops its traversal and terminatesthe vessel along that direction. Otherwise, if a branching pointis found, the smaller branch is trimmed off and the system con-tinues its traversal along the main vessel until a bifurcation or a

Fig. 6. Distribution of fb values over 20 randomly selected landmark points(ten branching and ten bifurcation points).

Fig. 7. Example showing the cutoffs at: (a) crossover point C; (b) bifurcationpoint Bi; and (c) branching point Br.

crossover point is encountered. To realize this, at each crossover(except the crossover to be examined) or bifurcation point, cut-offs are performed to separate its constituting segments whileat a branching point, the cutoff is to separate the smaller branchfrom the main vessel. This process is demonstrated in Fig. 7.Once the cutoffs are performed at all detected feature points, thesimplified segmentation is obtained by a morphological recon-struction starting at the crossing position, following the directionof its four segments and stopping when a cutoff is met.

This major vessel identification process helps to retain im-portant vessel structure while removing unnecessary structuresto simplify the subsequent analysis. Fig. 8 shows the exampleresults obtained through this process on the two crossover pointsin Fig. 1. We can see that this process has effectively removedsmall branches and noisy segments from the vascular network,retaining only important vessels constituting the crossover foraccurate AV nicking assessment.

To illustrate the effect of fb value on the resulting segmenta-tion, Fig. 9 shows the results obtained with two different settingsof fb on a crossover. On this example crossover, there are twobranching points (Br1 and Br2) on the vein vessel close tothe crossing region. The results show that if fb is set too small(i.e., fb = 0.1, Fig. 9(b)), the two small branches of the vein areincorrectly classified as bifurcation and this leads to the earlytermination of the system near the crossing location. This resultsin an inadequacy in the vascular network obtained since only asmall portion of the two venular segments are retained for further


Fig. 8. Example results of the major vessel identification process: (a) ini-tial segmentation; (b) skeleton image with detected branching points markedwith cross signs and detected bifurcation points marked with circles; (c) seg-mented image with cutoffs at branching and bifurcation points; (d) simplifiedsegmentation.

Fig. 9. Example results obtained after the major vessel identification processwith two different settings of fb : (a) cropped retinal image at a crossoverpoint (top) and its initial segmentation (bottom), (b) and (c): (top) segmentationwith cutoffs at the detected bifurcation and branching points and (bottom) itssimplified segmentation when (b) fb = 0.1 and (c) fb = 0.8.

analysis. On the other hand, when fb is set as 0.8 (see Fig. 9(c)),the two small branches of the vein are correctly identified andthey are successfully trimmed off from the main vessel. Thishelps to retain the most important parts of the vein and makethem available for the subsequent AV nicking measurement.

2) Venular Segment Identification: The simplified segmen-tation provides us with a vascular network containing four seg-ments; two associated with the vein and two associated withthe artery. Our analysis is performed on the vein, therefore weneed to determine which pair is associated to the vein. This isdone by an analysis on the vessel skeleton and edge images ex-tracted from the simplified segmentation. The vascular skeletondivides the boundary into four sections. For each section, theedge point closest to the crossing position is identified, resultingin four edge points: E1 , E2 , E3 , E4 (see Fig. 10(a)). Connect-ing these four points in a convex hull order will divide the wholesegmentation into five parts: the four vessel segments and the in-tersection region. The four segments are labeled in a clockwiseorder from S1 to S4 . Then, two opposite segments are paired torepresent each vessel, i.e., VSS1 = (S1 , S3), VSS2 = (S2 , S4).Fig. 10 depicts this identification process.

Fig. 10. Example showing the individual vessel identification process:(a) cutoff at the crossing region to separate the vascular network into four vesselsegments, labeled in a clockwise order (from S1 to S4 ); (b) and (c) two oppo-site segments are paired to represent each vessel: VSS1 = (S1 , S3 ), VSS2 =(S2 , S4 ).

Fig. 11. Example results of the venular segment identification process: (a)simplified segmentation with the cut-off at the crossover point; (b) AV classifiedimage (artery is in red, vein is in green); (c) extracted venular segments; (d)extracted venular skeleton and edge superimposed on the original image.

The AV classification process is then performed to distinguishthe vein from the artery. For the majority of crossover points, itis observed that the artery appears brighter than its vein counter-part. This is due to the fact that the artery contains oxygenatedblood which is pumped from the heart, making it red, whilethe vein carries deoxygenated blood back to the heart, whichmakes it darker. Hence, the color information of the two vesselsis computed and the artery is assigned to the vessel with higherintensity value. Different color spaces, RGB and HSV as wellas the gray level, were used to identify the most discriminativefeature. The median intensity value of each vessel is computedas

fVSS i= median(X(VSSi)) (3)

where i ∈ {1, 2}, X ∈ {R,G,B,H, S, V,Gray} representingdifferent color components. For example, R(VSS1) containsthe intensity values of the red channel of the first vessel. Then,the vein is identified as the vessel with lower intensity level.Once the vein is correctly recognized, its two segments areextracted for subsequent analysis. The results achieved throughthis process are shown in Fig. 11(a)–(d).

The artery-vein classification accuracy (ACC) is used to eval-uate the performance of our system at this stage. ACC is definedas the fraction of the number of crossover points where theartery and vein were correctly classified over the total numberof crossovers that were considered. Table I presents the ACC on90 working crossovers (described in Section III-A) using differ-ent color features. It is shown that the green channel provides


TABLE IARTERY-VEIN CLASSIFICATION ACCURACY (ACC) OF DIFFERENT COLOR

FEATURES ON 90 WORKING CROSSOVERS

Fig. 12. Method for identifying vessel boundary pixels representing the vesselwidth at a skeleton pixel.

highest accuracy, followed by the gray level. The red channeland the value component (in HSV space) yield similar goodperformance while the blue channel gives lowest accuracy. Thisresult is in accordance with previous studies ( [22]–[24]) wherethe green channel is found most effective for blood vessel ex-traction as it provides best contrast between the vessel and thebackground. This result further confirms that the green channelprovides best contrast between artery and vein and being themost discriminative feature for AV classification.

3) Vessel Width Measurement: In this step, the vessel widthsof the two venular segments are measured using the vessel skele-ton and edge images extracted in the previous step. For eachskeleton pixel Ci , a set of pairs of edge pixels whose connectedline going through Ci is identified (see Fig. 12)

E = {(E1j , E2k ) : Ci ∈ γE1j + (1 − γ)E2k} (4)

where Exi ∈ Ex, γ ∈ [0, 1], E1 , and E2 are the set of edgepixels at two sides of the vessel skeleton [see Fig. 12(a)]. LetCi−K and Ci+K be the two skeleton pixels at K pixel distanceto either side of Ci , the gradient at Ci can be estimated by thevector �Ci−K Ci+K . For each pair of edge points {E1j , E2k}, theangle of intersection with the skeleton is given as

cos (φjk ) =| �Ci−K Ci+K · �E1jE2k |∥∥∥ �Ci−K Ci+K

∥∥∥∥∥∥ �E1jE2k

∥∥∥. (5)

The two edge points {E1m ,E2n}, where

{m,n} = argminj,k

cos (φjk ) (6)

construct the line that is most perpendicular to the skeletongradient. We use the distance between these two edge points∥∥∥ �E1m E2n

∥∥∥ as the vessel width at Ci .

However, the vessel width at Ci is considered as valid onlyif φmn is close to 90o (i.e., |90 − φmn | ≤ δ). Otherwise, it ismarked as invalid and Ci is removed from the result to ex-

Fig. 13. Example showing the effect of δ on vessel width measurementsachieved: (a) cropped retinal image with superimposed vessel edge (white)and vessel skeleton (black); (b) vessel width measurements achieved withoutconstraint on φm n (δ = +∞); and (c) vessel width measurements achievedif δ is set as 5o . Invalid measurements are observed if no constraint is put onφm n while the setting of a small value of δ has effectively removed incorrectmeasurements.

Fig. 14. Example results of vessel width measurements produced by the pro-posed method on two example crossovers shown in Fig. 1: imaginary linesconnecting two edge points represent the vessel width at each skeleton pixel.

clude from further analysis. A small value of δ ensures that thevessel width achieved is perpendicular to the vessel axis. If noconstraint is put on φmn , the vessel width obtained might beincorrect due to the insufficient number of edge points to matchthe vessel width, which often happens at the skeleton pixelsclose to the crossing region, an example of which is shown inFig. 13.

Fig. 13(a) shows the vessel skeleton and vessel edge super-imposed on a venular segment while the vessel widths obtainedif no constraint is put on φmn (i.e., δ = +∞) is shown inFig. 13(b). Invalid measurements are found at the skeleton pix-els close to the crossover since they do not reflect the true widthof the vessel (they are not perpendicular to the vessel axis).These invalid measurements have been removed when δ is setas 5◦ (see Fig. 13(c)). Hence, it is set as 5o in our system. How-ever, it should be noted that similar results were observed fora range values of δ (5 ≤ δ ≤ 20). The vessel widths achievedon two example crossover points are shown in Fig. 14. We cansee that the straight lines representing the vessel width at eachskeleton pixel are perpendicular to the vessel axis while its twoedge points are fitted well on the true vessel boundary. This hasindicated that vessel widths were accurately measured.

4) AV Nicking Measurement: As each vein is composed oftwo segments at two sides of the crossing, the severity of AVnicking is assessed separately for each segment and the final AVnicking index of the crossover point is obtained by a combination


of these two individual measures:

AVN(C) = fA (AVN(V1), AVN(V2)) (7)

where fA is a coalescing function, AVN(V1) and AVN(V2) arethe AV nicking severity levels of the two venular segments,V1 and V2 , respectively. The method for computing the AVnicking severity level of each venular segment (at one side ofthe crossing) is as follows.

For each venular segment, four important measurements(W, WC , S, and Smissing ) are automatically derived from thevessel widths of that segment. W is the measurement represent-ing the normal vessel width of the segment while WC representsthe vessel width at the region close to the crossing location. Srepresents the total number vessel pixels at region close to thecrossover if there is no decrease in the vessel width (or the ab-sence of AV nicking), while Smissing represents the number ofvessel pixels that is missing due to the presence of AV nicking.These measurements are inspired based on the fact that there is adecrease in vessel width at the crossing region in the presence ofAV nicking, while this phenomenon is absent in normal cases.Hence, in the presence of AV nicking, WC is much lower thanthe normal vessel width W while the difference is not muchin the normal cases. Similarly, the number of missing pixelsSmissing is much higher in AV nicking crossovers than in nor-mal ones. The method for computing these measurements fromthe vessel widths is as follows.

Suppose that the segment is made up of n vessel width mea-surements, w = {wi |i ∈ {1, . . . , n}} (the vessel widths wi areindexed and ordered so that w1 is the width closest to thecrossover point, while wn is the furthest). W is approximatedas the median value of its measurements, or W = median(w).Suppose that N is the number of successive cross sections,starting from the first measurement w1 , with the measure-ments lower than normal vessel width W, WC is approxi-mated as the minimum value of the first N vessel widths, orWC = min{wi |i ∈ {1, . . . , N}}. The number of vessel pix-els at the crossing region in the normal case, S, is estimatedas N × W . Let S1 and S2 represent the number of missingpixels at both side of the vessel at the crossing region (seeFig. 15(a)), while S3 represents the number of vessel pixelscomposed by the first N cross sections (S3 =

∑Ni=1 wi), the

total number of missing pixels Smissing can be computed asSmissing = S1 + S2 = S − S3 = N × W −

∑Ni=1 wi . The il-

lustration of these measurements is shown in Fig. 15.From the four critical measurements (W, WC , S, and

Smissing ), three measures are derived to compute the AN nickingseverity of each venular segment as follows:

MMF =Smissing

S(8)

MR = 1 − WC

W(9)

MS = W − WC . (10)

All of these measures yield high values for AV nickingcrossovers and low values for normal cases. The first measureMMF evaluates the proportion of the missing pixels due to the

Fig. 15. Illustration of the measurements used for AV nicking computationon (a) synthetic vessels and (b) vessel width measurements. The definition ofthe annotations in this figure is as follows: W represents the vessel width in thenormal case; WC represents the vessel width at the crossing region; N is thenumber of successive cross sections, starting from the first measurement w1 ,with the measurements lower than normal vessel width W ; S represents thenumber of vessel pixels at the crossing region in the normal case (i.e., withoutAV nicking); S1 and S2 represent the number of missing pixels at both sideof the vessel at the crossing region; S3 represents the number of vessel pixelscomposed by the first N cross sections.

presence of AV nicking while MR measures the ratio of the ves-sel width at the crossing region compared to the normal width.MS measure computes the difference in the vessel widths at thecrossing region and the normal vessel width. For all of thesemeasures, the higher the value, the greater the severity of AVnicking at that crossover.

III. EXPERIMENTAL RESULTS

A. Material

The proposed method was evaluated using 47 high-resolutionretinal images obtained from two population-based studies, theBlue Mountain Eye Study (BMES) [25]–[27] and the SingaporeMalay Eye Study (SiMES) [28], [29]. BMES photos were takenin 35-mm color film, then digitized using CanoScan FS2710(Canon, Tokyo, Japan) set to automatic exposure and focus. Im-ages were converted to 24-bit (8-bit for each color space of red,green, and blue, with resolution 2720 dpi without enhancement,in Tagged Image File Format, image resolution of 3888 × 2592pixels). Retinal images in SiMES set were captured using a dig-ital nonmydriatic retinal camera (Canon CR-DGi, Japan) withthe image resolution of 3072 × 2048 pixels.

From these images, 90 detected crossover points were se-lected to evaluate the performance of the proposed method forAV nicking assessment. Each crossover point was manuallygraded by two experts at the Centre for Eye Research Australia(Melbourne, Australia) independently, using 4-scale gradingsystem (from 0 = normal to 3 = most severe). Disagreement be-tween two experts was then reassessed in a joint session, whichresulted in a single grading for each crossing point. We note thatthis evaluation process is very time consuming and expensivewith respect to graders time. Table II summarizes the distributionof AV nicking levels within 90 crossover points in this dataset.A set of four example crossover points with varying nickinglevels are shown in Fig. 16. The dataset of these 90 crossoverpoints with the manual assessment has been made publicly avail-able at: http://people.eng.unimelb.edu.au/thivun/projects/AV_nicking_quantification/.


TABLE IIDISTRIBUTION OF AV NICKING LEVELS ACROSS 90 WORKING CROSSOVERS

Fig. 16. Examples of crossover points with increasing AV nicking level, from(a) = 0 (normal) to (d) = 3 (most severe).

TABLE IIIPERFORMANCE IN TERMS OF SPEARMAN CORRELATION COEFFICIENT ρ

ACHIEVED BY THREE PROPOSED MEASURES ON 90 WORKING CROSSOVERS

B. Results and Discussions

In the first experiment, we examine the applicability of thethree proposed measures (MMF , MS , and MR ) for AV nickingassessment. For this, the correlation of the computed valuesand the manual grading was computed using the Spearman’srank correlation coefficient ρ. The Spearman coefficient ρ mea-sures the strength of association between two variables and itassesses how well the relationship between two variables can bedescribed using a monotonic function [30]. In addition, the signi-fication test result (P value) was reported to indicate if the corre-lation is significantly different from zero. The correlation scoresachieved by the three measures with different coalescing func-tions fA = {MIN, MAX, MEAN} are presented in Table III. Itshould be noted that fA is the coalescing function (7) that is usedto produce a single AV nicking level for each crossover usingthe AV nicking measurements of its two venular segments. Theresults show that the MS measure achieves highest correlationwith a correlation score of ρ = 0.70 (P < 0.0001) and whenMAX is used as the coalescing function. This means that theraw difference in the vessel width at crossing region and thenormal vessel width, which is captured by MS measure, is agood measurement of AV nicking. In addition, the severity levelat a crossover point should be assigned as the most severe ofits two individual segments (i.e., fA = MAX). It is interestingto see that MR measure is far less effective than MS measure.This means that the ratio between the normal vessel width andthe width at the crossing region (MR measures) is not effectivefor AV nicking measurement compared to the raw difference

Fig. 17. Box plot showing the linear relationship between the AV nickingvalues computed using MS measure against manual grading.

(MS measure). Finally, the fraction of the missing pixels at thecrossing region seems to be a poor measure for AV nickingassessment.

From the experiment above, we can see that MS is the mosteffective measure for AV nicking assessment. To visually assessthe results, the box plot in Fig. 17 shows the AV nicking valuescomputed using MS measure against the manual grading values.Each box in the box plot graphically summarizes the computedAV nicking values of crossovers with the same manual gradinglevel using five numbers: the red central mark is the median, theedges of the box are the 25th and 75th percentiles, the lowestand highest lines are the smallest, and largest computed values,respectively. It is shown that there is a clear linear associationbetween the subjective grading and objective measurements.In addition, it also indicates that the computed values differfrom subjective assessment mostly by one grading level, whichis reflected by small overlap in the computed values of twosuccessive grades, while they are well separated for grades oftwo level apart.

To roughly assess the performance of our system in terms ofclassification accuracy, we divide all crossovers into two classes(we refer to this as binary separation) by performing thresh-olding on the manual grading. With different threshold values,different separations are obtained. For example, the separation([0] vs. [1, 2, 3]) means that crossover points manually markedas 0 are classified as the first class while the remaining points areclassified as the second class. The performance in terms of ROCcurve is then evaluated for each such separation. In addition, theseparations are classified as 1-level separation and 2-level sep-aration if the difference in the upper bound value of the firstclass is one or two levels apart from the lower bound value


Fig. 18. ROC curves obtained on 1-level separations.

Fig. 19. ROC curves obtained on 2-level separations.

of the second class, respectively. For example, the separation([0, 1] vs. [2, 3]) is considered as 1-level separation while ([0]vs. [2, 3]) is considered as 2-level separation.

Figs. 18 and 19 show the ROC curves together with the areaunder the ROC curve (AUC) obtained on 1-level and 2-level sep-arations, respectively. The results show that high-classificationperformance are achieved for all separations (AUC ≥ 0.86). Inaddition, the ROC curves in Fig. 18 show that the separations(([0, 1] vs. [2, 3]) and ([0, 1, 2] vs. [3])) achieve higher per-formance than the ([0] vs. [1, 2, 3]) separation, which reflectsthat the system is more effective in detecting moderate to severeAV nicking cases than the mild cases. This is of clinical impor-tance since more severe AV nicking cases need to be effectivelyidentified for further examination. The results also show that theperformance achieved with 2-level separations are much higherthan with 1-level separations, which further confirms that thecomputed AV nicking values differ from subjective assessmentmostly at one grading level.

TABLE IVTHE PERFORMANCE OBTAINED BY THE SYSTEM ON DIFFERENT BINARY

SEPARATIONS IN TERMS OF MA, SEN, SPEC, AND κ

Table IV presents the performance of the proposed methodin terms of maximum accuracy (MA), sensitivity (SEN), speci-ficity (SPEC), and Kappa measure (κ) on different binary sep-arations. MA is defined as the maximum accuracy obtained inclassifying crossover points into two classes using the computedAV nicking values when compared with the expert grading. TheSEN, SPEC, and Kappa value are reported at the thresholdvalue that yields highest classification accuracy (i.e., MA). SENmeasures the proportion of actual positives which are correctlyidentified, while SPEC measures the proportion of negativeswhich are correctly identified. Kappa value κ is used to measurethe agreement in the grading of AV nicking produced by thesystem when compared with expert judgment [31].

The results show that the accuracy of our method at all sepa-rations is higher than 80%. In addition, the system can separatemoderate to severe cases from normal or mild cases (i.e., at ([0,1] vs. [2, 3]) separation) with an accuracy of 88% (SEN = 0.77,SPEC = 0.92, κ = 0.70). Higher performance is achieved inclassifying moderate to severe cases from normal cases (i.e.,at ([0] vs. [2, 3]) separation) with an accuracy of 89% (SEN= 0.85, SPEC = 0.92, κ = 0.76). Moderate agreements areachieved at two separations, ([0] vs. [1, 2, 3], κ = 0.59) and([0, 1, 2] vs. [3], κ = 0.52), while substantial agreements areobserved for the remaining separations (κ ≥ 0.70) (accordingto the interpretation of Kappa value κ in [31]).

IV. CONCLUSION

We have proposed and validated a new method for automatedAV nicking assessment. The accuracy of the method was eval-uated on 90 AV crossover points of 47 high-resolution retinalimages. The results show that the computed AV nicking mea-surements are strongly correlated with human judgment with acorrelation score of 0.70. In addition, the box plot and the ROCcurves indicate that the predicted values differ from manualgrading mostly at one grading level. In terms of classificationaccuracy, the proposed system can detect moderate and severeAV nicking cases with an accuracy of 88% (SEN of 0.77, SPECof 0.92, Kappa value κ of 0.70). These have demonstrated thereliability and accuracy of the proposed system for AV nick-ing assessment. The measurements produced by the proposedmethod provide the basis toward the development of a system forautomatic AV nicking detection for a large-scale screening sys-tem. Moreover, since the quantified values provide more detailson the AV nicking severity level, the computed measurementsmay help to enhance the relationship between AV nicking and


known diseases such as hypertension and stroke. At present, weare applying our method on a large number of images to deter-mine AV nicking severity level associated with cardiovasculardiseases. The results will be reported later to the appropriateclinical journal.

ACKNOWLEDGMENT

The authors wish to thank to K. Y. Lee and L. Hodgson at theCentre for Eye Research Australia (Melbourne, Australia) forkindly providing us with the arteriovenous nicking grading andclinical advice.

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Authors’ photographs and biographies not available at the time of publication.

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