automatic pixel-parallel extraction of the retinal ... · automatic pixel-parallel extraction of...

IntroductionPixel Level Snakes (PLS)

AlgorithmExperimental results

ApplicationsConclusions

Universidade da Coruna

Automatic Pixel-Parallel Extraction of the Retinal VascularTree: Algorithm Design, On-Chip Implementation and

Applications

Carmen Alonso Montes

July 18th, 2008

Supervisors: Manuel Gonzalez Penedo and David Lopez Vilarino

Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n




Motivation

Retinal vessel treeMedical research

early diagnosispatient monitoring

Biometric researchRetinal vessel patternAuthentication applications

Bottleneck : High computation effort required for the extraction of the retinalvessel treeGoals of this thesis:

Design of an algorithm to extract the retinal vessel tree at a high computation speedPixel-parallel approachCustomization of the algorithm and tuning the main parameters for the retinal vessel treeextraction taskAnalysis of the reliability (accuracy) and time performanceAn implementable algorithm into a processor array with SIMD processing capabilitiesIntegration of the algorithm into practical applications





Outline

1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach

2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach

3 Pixel parallel retinal vessel tree extraction algorithm

4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique

5 ApplicationsAuthentication applicationsAVR ratio estimation application

6 Conclusions





Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach

Outline






6 Conclusions






Medical images

Human eye cross-sectional view Non-Mydriatic Canon CR6-45NM

Fundus retinal images

Cameras capture ultra high-resolution digital images

Types:

MydriaticNon-mydriatic

Non-mydriatic are the most commonly used since it is not necessary to use dilation drops






Types of digital retinal images

Digital fluorescing angiography

Dying trace method

High risk of adverseeffects on the patient

The most intrusivetechnique

Colour fundus photography

Uses a white zenon flashlight

Usually in RGB format,and channel G is selectedto get the gray scaleimage

Red free photography

Invisible infrared light toilluminate the retina

The patient does notexperience blinding whitelight during this process






Image features

Several vessel widths

Vessels are usually low contrast, particularly narrow vessels






Image features

A high variability of vessel widths

Vessels are usually low contrast, particularly narrow vessels

Variety of structures:

retina boundaryoptic diskpathologies

Central reflex which causes a complicated intensity cross-section






State of the art

Classification of retinal vessel tree extraction techniques:

Pattern Recognition , like matched filters, adaptive threshold, or region-based approaches

Model-based approaches, which include classical or geometric deformable models

Tracking-based approaches

Artificial intelligence-based approaches

Neural network approaches

Tube-like object detection approaches

Main drawbacks

High execution time, specially regarding real-time requirements

The definition and tuning of parameters is complex in some approaches






The most flexible tools to deal with the extraction of the retinal vessel tree are

Active contours

AdvantagesReasonably management of

NoiseAmbiguous boundaries

DrawbacksInteractive tool (not automatic)A strategy to compute the initial conditions must be definedHigh computation effort

SolutionCellular Active Contours






Our proposal

Our proposal

Pixel Level Snakes (PLS)

Resolve the high computational cost of classic active contour techniquesBased on pixel level discretization of the the contoursMassively parallel computation on every contour cellImplemented on hardware architectures with SIMD capabilities (ACE4K, SCAMP-3 andspecific purpose integrated circuits)

Automatic computation of the initial conditions needed by PLS






Our proposal

Our proposal

Strategy: Fitting the exterior of the vessels

Robust control of the evolutionEasier initialisation (only 12.7% of pixels belong to vessels)

Implementable in a SIMD processor array





Contour-based approachRegion-based approach

Outline






6 Conclusions






Active contours: an overview

Elastic curveu(s) = (x(s), y(s)), s ∈ [0, 1]

Evolves from its initial shape and position as a result of the combined action ofExternal forces : Guide the contours towards the features of interestInternal forces : Control the smoothness of the contour

Main input imagesInitial contourExternal potential image (guiding information image)






PLS

Pixel Level Snakes

Three modules which interactdynamically:

Guiding InformationExtraction

information to guide theevolution

Contour Evolution

pixel-to-pixel shift of thecontours

Topological Transformation

SplittingMerging

Mathematical definition of the potential field

P(x, y) = kint Pint (x, y) + kext Pext (x, y)






PLS: evolution with external potential

External Potential

External Potential

External potential guides the contours towards the boundaries of interest






PLS: evolution with internal potential

Internal Potential

Internal Potential

Internal potential maintains the smoothness of the contour






PLS: evolution with balloon potential

Balloon Potential

Guiding forces with the balloon potential

Balloon potential moves the contours when the external potential is too weak

Final mathematical definition of the potential

P(x, y) = kint Pint (x, y) + kext Pext (x, y) + kinf Pinf (x, y)






Contour-based PLS

Contour-based PLS

Fully operative PLSimplementation

DCD module

controls the contourevolution

GFE module

computes the guidinginformation

IPE module

computes the internalpotential

Recursive low-passfilteringDiffusion operation

Evolution in the four cardinaldirections






Contour-based PLS

Topological Transformations

Topological changes

Preservation of thetopologySplitting and merging

CPD module

Manages the topologicaloperations

Hole filling operation

High computation effort






Region-based PLS

Region-based PLS

Boundaries of an active region

Eight movements in the four cardinaldirection are needed for a whole PLScycle

1 North-South-East-West2 Inversion of the regions3 North-South-East-West4 Inversion of the regions






Region-based PLS

Topological transformations

No extra computation isneeded

No hole filling operation






PLS summary

Both proposals have been considered in the design of the algorithmContour based PLS

A robust control over the evolution is providedFaster evolution (4 movements in each PLS cycle)

Region based PLSThe topological changes are easily made

Drawbacks

More accurate external potential is needed

Apparently slower, since 8 movements are required in a PLS cycle

Advantages

Easily customizable for a particular task, if only inflation/deflation is needed





Outline






6 Conclusions





Goal

Automatic computation of the initial conditions from the original image

Tuning the parameters to fit the vessels

Calibration of the PLS parameters

An implementable HW version for a pixel parallel processor





Approaches to solve the task

The first attempt consists on implementing the steps in terms of local operations

convolutionsarithmetic and logical operations

Cellular Neural Network (CNN) based implementation has been proposed

Straightforward methodology for image processing techniquesKey : improving the computation time provided by massively parallel processing

Problem : Some of the steps initially proposed cannot be implemented in a processor arraydue its complexity





Conceptual definition of the stages

Stage 1 : Vessel pre-estimation

Determination of the vessel locationsPre-filtering steps to improve the signal-to-noise ratio

Stage 2 : Initial contour estimation

The initial conditions for PLS

Stage 3 : External potential estimation

Computation of the guiding information

Stage 4 : PLS evolution

Calibration of PLS





Stage 1: Vessel Pre-estimation (First approach)

Original image conditions

Non uniformity in the gray level values along the vessels

Ambiguity in the vessel boundaries

Noise






Histogram Equalization

Goal : Improving lowcontrast vessels

Adaptive segmentation

Goal : Computation of anoptimal local threshold

Opening

Goal : Removing isolatednoisy points






Adaptive Segmentation

CNN-based adaptive segmentation addressed in Rekeczky et al. [1] was proposed.

It consists on a local threshold estimation followed by a locally adaptive segmentation.

Test = αEm + βEv + thres, α ∈ [0, 1], β ∈ [−1, 0]

Em and Ev are the mean and the variance estimations of the considered image

thres is a constant threshold value which depends on the gray-level of the considered image

α and β are scale factors






Adaptive Segmentation

CNN-based adaptive segmentation addressed in Rekeczky et al. [1] was proposed.

It consists on a local threshold estimation followed by a locally adaptive segmentation.

Test = αEm + βEv + thres, α ∈ [0, 1], β ∈ [−1, 0]

thres = max[

∑ Ni=1 Ii1N

,

∑ Ni=1 Ii2N

, . . . ,

∑Ni=1 IiMN

]

Bigger proportion of background pixels than foreground

Mean value of the columns gives a threshold value closer to the local gray level value





Stage 1: Vessel Pre-estimation (Final version)

Final approach

Histogram equalization has been discarded, since noise is also enhanced

An adaptive segmentation is needed

Diffusion gives a suitable local threshold

The substraction of the original and diffused image gets a suitable segmentation of the image

A local threshold value is used to refine the final result





Stage 2: Initial contour estimation

1st Approach

Computation of suitable initial conditions for the vessel removing vessel discontinuities

We need to assure that the initial image needed by PLS are completely outside of the vessellocations

Contour-based PLS

Step 1. Dilation : Several dilations are actually needed to remove the discontinuitiesStep 2. Binary edge detection : To get the initial contours

Fitting the exterior of the vessels simplifies the computation of the initial contours

12.7% of pixels belongs to vessels





Stage 3: External potential estimation (First approach)

1st Approach

Both images contain the needed information in order to stop the PLS evolution in thevessel boundaries

Iext = ρIeq + δIop

ρ and δ are scale factors

Disadvantage : External potential image can be computed in a more accurate way





Stage 3. External potential estimation (Final approach)

Final Approach

Applying Sobel operator accurate edges are obtained, but:weak vessels are not properly segmentedvessels topology is neither maintained nor assured






Final Approach

The image segmented in Stage 1 contains more vessel information and also noise

The combination of both results will gives more robustness to control PLSevolution






Final Approach

A distance estimation is made to guide the evolution towards the vessel locations

Several dilations are performed to compute the distance map






Final Approach

A diffusion step is performed to smooth the values

The diffusion step leads towards a loose of accuracy of the boundary location

Edges must be emphasized





Stage 4: PLS evolution

Goals

Input images have been automatically computed from local statistics of the original image

Main parameters of PLS should be calibrated to control the evolution towards the vessels

Good results have been obtained from the image processing point of view






Final Approach

The input images previously computed are used by PLS to fit the vessel edges

PLS parameters were calibrated

This stage has been split up into several steps to get a better control over the evolution






1st PLS Step

Balloon Potential has a high relevance compared to the other potentials since vessel locationsare far away

Region merging is enabled






Hole Filling operation

This operation is used to remove internal regions appeared due to noise, segmented duringthe previous stages

This operation is applied only once






2nd PLS Step

The PLS evolution is guided basically by the external potential due to the proximity to thevessel locations

Region merging is disabled to maintain vessel topology





Final algorithm: General scheme





Summary

Original approach Final approachComputing platform CNN SIMDHW implementation Partial CompleteContour representation Contour RegionDesign General purpose Specific purposeExecution time of a PLS cycle 518 µ s. 273 µ s.

Remarks

The final approach is fully implementable on a SIMD processor array

The original version consists on a general purpose version, whereas the final one has beenspecifically tuned for the retinal vessel tree extraction task

Notice that the execution time required for PLS cycle has been customized for this particulartask

Only inflation forces are actually needed

Fitting the exterior of the vessels provides a robust control over PLS evolution

All the potentials (forces) have been considered for the evolution





Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique

Outline






6 Conclusions






Remarks

Due to the high resolution of the retinal images, they have been split up into sub windows

The maximum size allowed in the chip implementations, used in this thesis, is 128x128

The final result is obtained by means of the union of all the sub windows






DRIVE database

DRIVE: Digital Retinal Images for Vessel Extraction database

40 images available (7 images with pathologies and 33 images without diseases) with aresolution of 768x584

This database is used for the general analysis of algorithms for the retinal vessel extraction

Only the pixels inside the FOV are actually used to compute the accuracy

The inter-observer agreement is better than in other databases, such as the STARE






Experiment design

Software: MATLAB environment

Images from the DRIVE database

Parameters in the stages:

Stage 1

Threshold value established to 5 to refine the results

Stage 2 & 3

A total of 4 erosion steps have been performed

Stage 4

6 cycles were used for the first PLS step

External Pot. Internal Pot. Balloon Pot. No. Cycles1st PLS step 100 % 1% 60% 62nd PLS step 100 % 30% 5% *1

Parameters shown in the table has been tuned only once for all the images *1A convergence control has been implemented, so this number of cycles is not fixed






Analysis of the accuracy

Maximum Average Accuracy (MAA)

Accuracy =Tpos + Tneg

NP

Tpos is the vessel (true positive) correctlyclassified pixelsTneg is the non-vessel (true negative)correctly classified pixelsNP is the number of pixels considered intothe FOV region

A total number of 20 images (from the test set)has been used

The manual segmentation of the second observerhas been used as the gold standard

Method MAAManual Method 0.9473Soares [2] 0.9466Al-Rawi [3] 0.9458Kirsch [4] 0.9151Staal [5] 0.9611Chaudhuri [6] 0.8773Proposed algorithm 0.9180






SCAMP-3 vision system

Scamp-3 vision system

It provides a high-performance low-powersolution for computer vision applications

The processor array operates in SIMD

The processing elements simultaneouslyexecute identical instructions on their localdata






SCAMP-3 vision system

Scamp-3 vision system

The SCAMP-3 vision system executes asequence of simple array instructions

additioninversionone-neighbour access

It operates in a pixel-parallel fashion on128x128 arrays

1.25 MOPS per pixel

Development software and simulatorenvironment

250mW power consumption at themaximum processing






SCAMP implementation






Stage 1: Vessel region pre-estimation

Notes

The blurring effect is computed by means of a fast diffusion

The fast diffusion is implemented on the SCAMP via a resistive grid structure

The threshold value is established to zero

The boundary segmentation effect is due to a zero-padded boundaries from thediffusion operation






SCAMP implementation






Stage 3: External Potential estimation

Notes

This stage has been adapted to the specific performance of the SCAMP

The edge detection over the segmented image is not actually required

The distance estimation is given by the combination of the Sobel and the segmented images

This definition allows us to take advantage of the specific performance of the SCAMP system






Results using the SCAMP-3 vision system chip

Original image Stage 1 Stage 2 Stage 3 Stage 4






Execution time required for a 128x128 sub window

No. Stage Stage Exec. Time ( µs)1 Vessel Region Pre-estimation 12.82 Initial Region Estimation 55.23 External Potential Estimation 134.4

41st PLS Step (6 cycles) 518Hole Filling 1954.52nd PLS Step (40 cycles) 3870.8

Analysis of the execution time

128x128 windows are considered to perform the algorithm in the SCAMP

The I/O time required is 1.25 ms

The execution time for a sub window is 6.5 ms

The global execution time for a retinal image is about 0.1925 s (approximately 30 subwindows)






Summary

Method MAA Exec. TimeManual Method 0.9473 2 h.Soares [2] 0.9466 3 min.Al-Rawi [3] 0.9458 5 s.Kirsch [4] 0.9151 2 s.Staal [5] 0.9611 15 min.Chaudhuri [6] 0.8773 5 s.Proposed algorithm 0.9180 0.1925 s.

Analysis of the execution time

The MAA is suitable for many practical applications

A very fast system compared with standard approaches

The proposed algorithm has been recently implemented in C, using Microsoft Visual Studio9.0

The execution time required is 6.64 s in a PC with a Intel 2 Core Duo processor at 2.10GHzNo sub windowing is needed in this case






Overlapping

Goal

This technique has been analyzed to study the improvement of the global MAA value

Rows and columns of the sub windows are overlapped to get redundant information in thelimit areas

The importance of the pixel information depends on the position of that pixel in the sub window






Column overlapping

Column Overlapping

Columns are overlapped, and the value of the pixels are weighted according to its position insidethe overlapping area






Row overlapping

Row Overlapping

Rows are overlapped, and the value of the pixels are weighted according to its position inside theoverlapping area






Application of the overlapping to the results of the algorithm






0

0.2

0.4

0.6

0.8

1

64 32 16 8 4 0

No. Pixels used in the Overlapping

LegendSensitivitySpecificityAccuracy

0

0.2

0.4

0.6

0.8

1

64 32 16 8 4

Exe

c. T

ime

(s.)

No. Pixels used in the Overlapping

LegendExecution Time

Conclusions

The overlapping technique is not actually needed due to the remarkable increment onthe execution time and the slightly improvement in the other factors.





Authentication applicationsAVR ratio estimation application

Outline






6 Conclusions






Retinal vessel tree is used in a wide range of practical applicationsMedical researchMedical systems use the retinal vessel features for example in early diagnosis, related with:

stenosismalformationscardiovascular risk

Biometric authentication

Vessel-pattern is being used for authentication systems due to its robustness againstforgery

The pixel-parallel tree extraction algorithm

The algorithm proposed in this thesis has been included into the following applications:

Authentication applications:

Creases-based authentication systemPoint feature-based authentication system

Medical applications:

Arteriolar-to-venular diameter ratio estimation






Traditional modes of authentication

Physical possessions : keys, passports, smart cards

Knowledge : password, pass phrases

Biometric features : physiological and behavioral characteristics of individuals thatdistinguish one person from the next

Characteristics for a biometric feature

Universality

Uniqueness

Permanence: invariant over the time

Collectability: it should be measurable

Acceptability by the users / individuals






The pixel parallel retinal vessel extraction algorithm in theauthentication systems

Notes

Two authentication systems have been considered to integrate the pixel parallel approach 1

Goal: Improving the computation time for obtaining the retinal vessel tree

Both of these systems use the skeleton instead of the retinal vessel tree

An skeletonisation step has been included to process the output of the pixel-parallel algorithm

1The authentication systems is the basis of the thesis of Marcos Ortega Hortas






Authentication system using creases

Image registration

Steps for the computation of the similarity value:

Alignment of the image under study and the reference image

Normalized cross-correlation function:

γ =

∑

x,y [f (x, y) − f ][g(x, y) − g]√

[f (x, y) − f ]2[g(x, y) − g]2

g is the mean of the registered imagef is the mean of the image under studyOnly the pixels belonging to the overlapping area are not null

A threshold is defined to distinguish the individuals

If γ is higher than the threshold that means that both images belong to the sameindividual, and vice versa






Authentication system using point feature extraction







Feature point extraction

Feature points are:

Ridge endingsBifurcations of the vessels

Some steps have been defined

1 Segment detectionDetecting the segmentsLabeling the segments

2 Detection of union and bifurcations3 Feature point sets is computed

Unions

Endpoints are close to each other and they havesimilar orientations with a smooth connection

Bifurcations

Compute the endpoint directionExtend the segment in that directionIf a segment is found in that direction, the bifurcationis tagged







Registration process

Transformation of the acquired image in order to align its feature points with thereference imageTransformation considered is the Similarity Transformation (ST) which can handle:

TranslationIsotropic scalingRotation







Matching

Similarity between points (A and B) stands for the maximum distance allowed

S(A, B) = 1 −distance(A, B)

D

If two points have a similar value to the reference point, the best match iscomputed by meas of the probability of correspondence

The matching value is computed as follows

1√

MN

∑

(i,j)∈Q

S(Ai , Bj )

The matching value is compared with the threshold value for the acceptance orrejection






Experiment design

Blind test has been designed

100 images (12 of them belonging to different individuals)

Image resolution is 768x584 pixels, which implies a total number of 30 subwindows

The execution time needed for the extract the retinal vessel tree is 0.1925 s. (6.5ms for each sub window)






Experimental results







0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Similarity Threshold for Acceptance

Err

or R

ate

FAR

FRR

ConfidenceBand

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Threshold

Per

cent

age

FARFRRERR

Conclusions

False Acceptance (FAR) and False Rejection (FRR) rates can be reduced to 0 (FAR = FRR)

Equal Error Rate (EER) = 0 which implies a 100% of effectiveness

The mean execution time is about 0.19 s to get the skeleton, 250 ms. for the authenticationstage

The whole execution time for the authentication system is 0.44 s.






Medical applications

Vessel geometry are the basis of medical applications related with:early diagnosiseffective monitoring of therapies in retinopathy

The Arteriolar-to-Venular ratio (AVR) is used to establish the cardiovascular risk,for example

The pixel-parallel algorithm

The pixel parallel algorithm proposed in this thesis has been integrated into theSIRIUS web application (System for the Integration of Retinal ImagesUnderstanding Services)

5 hospitals in Galicia are currently using this web application






SIRIUS and the pixel parallel algorithm

Steps in the system

1 Selection of the retinal imageby the specialist

2 Concurrently extraction of theretinal vessel tree

3 Selection of the optic disk

4 Drawing three circles,concentric to the optic disk

5 Obtaining crossing points






Remark

The segment which joins the points must be perpendicular to the centreline of thevessel






Steps in the system





5 Obtaining crossing points areobtained

6 Estimation of the vesseldiameter






Vessel width

W =

3∑

i=1

li /3

Euclidean distance

l1 =√

(xA − xB)2 + (yA − yB)2






Steps in the system





5 Obtaining crossing points areobtained

6 Estimation of the vesseldiameter

7 Manual classification of the twotypes of vessels into vein orartery

8 Computation of the AVR ratio






Vessel width

W =

3∑

i=1

li /3

Euclidean distance

l1 =√

(xA − xB)2 + (yA − yB)2

AVR ratio

AVR =

∑

Wa∑

Wv







No. Image Caderno et al. [7] Proposed algorithm1 0.79 0.782 0.82 0.843 0.81 0.824 0.80 0.765 0.82 0.806 0.89 0.917 0.77 0.778 0.90 0.939 0.83 0.8010 0.83 0.81

Experimental conditions

Images obtained using the Cannon CR6-45NM Non-Mydriatic Retinal Camera

Ten images with a resolution of 768x584 pixels

The pixel parallel retinal vessel tree extraction algorithm takes 0.19 s for the wholeangiography

The proposal of Caderno et al. [7] takes 32.1 s for extracting the whole retinal vessel tree





Outline






6 Conclusions





A novel algorithm for retinal vessel tree extraction has been presentedIt has been implemented in terms of local operations and convolutionsImage processing point of view

DRIVE database has been used to test the reliability of the proposed algorithmMAA obtained shows that the results of the proposed algorithm is enough for manypractical applications

Time performance point of viewThe algorithm has been implemented in the SCAMP systemThe execution time study shows that it is faster than conventional PC-based techniques

This algorithm has been successfully integrated into practical applicationsAuthentication applications A 100% of effectiveness is maintained in theauthentication processAVR ratio estimation Faster computation of the AVR ratio with similar results

Future research

Integration of this algorithm into

Applications with fast computation requirementsvideo-based applications (tracking)

Projection onto a focal plane processing systems

Full integration with other devices for making a portable device

The evolution on processor arrays in the increment of their size will balance the distancebetween PC-based and Hardware-based solutions





Rekeczky, C., Schultz, A., Szatmari, I., Roska, T., Chua, L.O.:Image Segmentation and Edge Detection via Constrained Diffusion and AdaptiveMorphology: a CNN approach to Bubble/debris Image Enhancement.In: Proc. 6th Int. Symp. Nonlinear Theory and its Applications (NOLTA ’97). (1997)209–212

Soares, J.V.B., Leandro, J.J.G., Cesar, R.M., Jelinek, H.F., Cree, M.J.:Retinal Vessel Segmentation using the 2-D Gabor Wavelet and SupervisedClassification.IEEE Trans. Med. Imag. 25 (2006) 1214–1222

Al-Rawi, M., Qutaishat, M., Arrar, M.:An improved matched filter for blood vessel detection of digital retinal images.Comput. Biol. Med. 37(2) (2007) 262–267

Kirsch, R.A.:Computer Determination of the Constituent Structure of Biological Images.Comp. Biomed. Res. 4(3) (1971) 315–328

Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.:Ridge-Based Vessel Segmentation in Color images of the Retina.IEEE Trans. Med. Imag. 23 (2004) 501–509





Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.:Detection of Blood Vessels in Retinal Images using Two-Dimensional MatchedFilters.IEEE Trans. Med. Imag. 8 (1989) 263–269

Caderno, I.G., Penedo, M.G., Marino, C., Carreira, M.J., Gomez-Ulla, F.,Gonzalez, F.:Automatic Extraction of the Retina AV Index.In: LNCS Int. Conf. Image Analysis and Recog. (ICIAR’04). Volume 3212. (2004)132–140


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