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Ko
etal
.
BioMedicalEngineeringOnLine
2010,
9
:54
http://www.biomedical-engineering-online.com/content/9/1/54

RESEARCH

OpenAccess

Imageresizingusingsaliencystrengthmapand
seamcarvingforwhitebloodcellanalysis
ByoungChulKo,SeongHoonKim,JaeYealNam
*

*Correspondence:jynam@kmu.ac.
rkDepartmentofComputer
Engineering,KeimyungUniversity,
Shindang-Dong,Dalseo-Gu,Daegu,
areKo

Abstract
Background:
Anewimage-resizingmethodusingseamcarvingandaSaliency
StrengthMap(SSM)isproposedtopreserveimportantcontents,suchaswhiteblood
cellsincludedinbloodcellimages.
Methods:
Toapplyseamcarvingtocellimages,aSSMisinitiallygeneratedusinga
visualattentionmodelandthestructuralpropertiesofwhitebloodcellsarethen
usedtocreateanenergymapforseamcarving.Asaresult,theenergymap
maximizestheenergiesofthewhitebloodcells,whileminimizingtheenergiesof
theredbloodcellsandbackground.Thus,theuseofaSSMallowstheproposed
methodtoreducetheimagesizeefficiently,whilepreservingtheimportantwhite
bloodcells.
Results:
ExperimentalresultsusingthePSNR(PeakSignal-to-NoiseRatio)andROD
(RatioofDistortion)ofbloodcellimagesconfirmthattheproposedmethodisable
toproducebetterresizingresultsthanconventionalmethods,astheseamcarvingis
performedbasedonanSSMandenergymap.
Conclusions:
Forfurtherimprovement,afastermedicalimageresizingmethodis
currentlybeinginvestigatedtoreducethecomputationtime,whilemaintainingthe
sameimagequality.

Background
Peripheralbloodcelldifferentialcountingprovidesvaluableinformationforaccurate
patientdiagnoses,yetthemicroscopicreviewislaborintensiveandrequiresahighly
trainedexpert.Currentautomatedcellcountersarebasedonlaser-lightscatterand
flow-cytochemicalprinciples,nonetheless,21%ofallprocessedbloodsamplesstill
requiremicroscopicreviewbyexperts[1].Therefore,variousefforts[1-5]havealready
beenmadetodevelopautomaticcellanalysissystemsusingimageprocessing.Blood
cellimagesconsistofbothwhiteandredbloodcellsscatteredacrosstheentireimage,
however,itisthewhitebloodcells(WBCs)thatprovidetheimportantinformationfor
patientdiagnoses,suchasleukemiaorcancer[2].Thus,inmostresearch,WBC
segmentationistheimportantprocedure,wheretheultimategoalistoextractallthe
WBCsfromacomplicatedbackgroundandthensegmenttheWBCsintotheir
morphologicalcomponents,suchasthenucleusandcytoplasm.
RepresentativeWBCanalysissystems,suchasCellarvisionDiffmasterOctavia[4]and
CellarvisionDM96[5],scanthewholeslideatalowmagnificationfirsttoidentify
potentialWBCsusingthespecificcharacteristicsofWBCs,suchastheircolor,size,and

©2010Koetal;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreativeCommons
AttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,andreproductionin
anymedium,providedtheoriginalworkisproperlycited.

Ko
etal
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BioMedicalEngineeringOnLine
2010,
9
:54
http://www.biomedical-engineering-online.com/content/9/1/54

shape,andthentakedigitalimagesatahighmagnification.Thereafter,pre-classification
isperformedusingonlythecroppeddigitalimages.Whilethismethodismoreefficient
thanscanningWBCsfromahigh-resolutionimageofthewholeslide,additionaltimeis
requiredfortheWBCsearch,especiallywhentheimagecontainsseveralWBCs.
Furthermore,additionalstorageisneededtosavetheindividualpotentialWBCsand
extratimerequiredtoclassifytheWBCs,asthesystemhastocheckallpotentialWBC
imagestoanalyzejustoneslide.
Meanwhile,othermethods[2,3]useonlyanoriginalhigh-resolutionimageforthe
WBCanalysis.However,analyzingWBCsfromthewholeimageistimeconsuming,
sincethesizeofbloodcellimagesisnormallyatleast800×600.Therefore,animage-
resizingmethodisneededthatretainsalltheWBCswithoutmorphologicaldistortion
inordertoreducethepost-segmentationclassificationtime.Furthermore,sinceresized
high-qualityimagesrequirelessstorage,thepost-imagesegmentationandclassification
canbemoreaccuratethanwithconventionalimagecompression,suchasJPEG.
Relatedworkcanbedividedintotwoparts;imagecompressionandimageresizing.
First,variouslosslesscompressiontechniquesalreadyexistthatcanpreservethe
characteristicsofanimage,yetwithalowcompressionrate.Forexample,several
researchers[6-8]haveproposedtransformcodingschemes,suchasaPrincipalCom-
ponentAnalysis(PCA)andDiscreteCosineTransform(DCT),whileKarrasetal.[9]
usedadiscretewavelettransformation(DWT)andfuzzyc-meansclusteringtechnique.
Plus,toachievehighercompressionrateswithoutdetractingfromthequality,region
ofinterest(ROI)methodswithaDCThavealsobeeninvestigated[6,10].Inparticular,
Gokturketal.[10]proposedahybridmodel,usinglosslesscompressioninregionsof
interestandhigh-ratemotion-compensatedlossycompressioninotherregionsinthe
caseofasequenceofCTimages.Nonetheless,eventhoughlosslesscompressionpro-
ducesahighercompressionratewithoutdistortingROIs,theexactpreservationofa
ROIisstilldifficultwhenthecompressionrateisaboveaspecificlimitation.Therefore,
anewalgorithmisneededthatcanefficientlypreserveROIs,regardlessofthecom-
pressionrate.
Inadditiontoimagecompressionmethodsthatmerelypreservetheoriginalimage
size,someresearchershaveattemptedtoresizeorcrop[11,12]imagesaccordingto
theimagecontents.Yet,asshowninFig.1-(b),standardresizinghomogeneously
reducestheimagesize,therebydamagingalltheimagecontentsbasedontheratioof
theresizing.Similarly,whilecroppingcanbeusedtodisplaythemostimportant
regioninanimage,asshowninFig.1-(c),cellimagescontainmanyROIs,making
croppinginappropriateforcellimagecompression.
Meanwhile,seamcarving[13]changesthesizeofanimagebysubtlyremovingor
insertingaconnectedpathofpixelsfromadifferentpartoftheimageaccordingtothe
measuredenergy,asshowninFig.2-(b).However,eventhoughseamcarvingcaneffi-
cientlyremovenon-ROIpixels,iftheoperationisappliedtooharshly(i.e.resizingan
800×600imageto200×120),importantROIscanstillbedamaged,asshowninFig.
2-(d).Furthermore,sinceseamcarvingwasoriginallydevelopedfornatureimages,its
applicationtomedicalimagesissomewhatlimited.Forexample,theenergydistribu-
tionofWBCsisnotdistinctiveinbloodimages,makingithardtoapplyaseamcar-
vingoperatortobloodcellimages,whichresultsinaninevitableremovalorinsertion
ofpixelsinWBCs.

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2010,
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Figure1
Examplesofdifferentscaling-downmethods
.

Accordingly,thispaperpresentsanewmethodforresizingbloodcellimageswhile
preservingthesizeandshapeofWBCs.Inperipheralblood,WBCsaredividedinto
fiveclassesaccordingtotheirmaturationstage,makingitessentialtopreservethesize
andshapeofthenucleus.Thus,toprovideanefficientimage-resizingmethodthat
treatsWBCsasROIs,aSaliencyStrengthMap(SSM)isproposedusingavisualatten-
tionmodelandthestructuralpropertiesofWBCstogenerateanewenergymap.As
such,thismapmaximizestheenergiesoftheWBCs,whileminimizingtheenergiesof
theredbloodcellsandbackground.Therefore,incontrasttopreviousalgorithms,the
maincontributionofthisstudyistoimprovetheresizingperformancewithalower
filesize,whilepreservingtheWBCsusingtheproposedSSMwithanenergymap.
Fig.3showsthearchitectureofthecellimageresizingusingtheproposedSSMand
seamcarving.
Theremainderofthispaperisorganizedasfollows.
Methods
describesthealgo-
rithmsusedtocreatethesaliencystrengthmap,anEllipseAttentionWindow(EAW)
thatremovesuselessregionsfromtheimage,andtheseamremovalusinganenergy
mapbasedonthesaliencystrength.
ResultsandDiscussion
evaluatestheaccuracy

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BioMedicalEngineeringOnLine
2010,
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Figure2
Exampleofseamcarving
:(a)originalimage,(b)energymapof(a),(c)reducedimageusing
seamcarvingthatpreservesshapeofimportantcontents(WBCs),(d)distortedimageusingharshseam
carving,whereshapesofWBCnucleiaredistorted.

Figure3
Architectureofproposedmethod
.

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2010,
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andapplicabilityoftheproposedresizingmethodbasedonexperiments,andsome
finalconclusionsandareasforfutureworkarepresentedin
Conclusions
.
Methods
Thispaperproposesanewimage-resizingmethodwithalowerfilesizethatcanefficiently
preserveWBCsusingavisualsaliencymapbasedonthefollowingtwoassumptions:


thenucleiofWBCsarenearlyround.

thenucleiofWBCsarehighlightedinpurpleonawhitebackgroundwithmono-
chromaticredbloodcells.

Usingthesecharacteristics,aSaliencyStrengthMap(SSM)isproposedusingavisual
attentionmodel,whilethestructuralpropertiesofWBCsareusedtogeneratean
energymap.
Saliencymapgeneration
Incontrasttonatureimages,microscopicimages,especiallybloodcellimages,have
differentcharacteristicswithdistinctdiagnosticmeanings,suchasavaryingcolorand
saturationaccordingtofluorescencestaining.Forexample,inthecaseofbloodcell
images,thesalientparts,theWBCs,tendtobehighlysaturatedandpurpleincolor,
whiletheremainingparts,theredbloodcells,haveamoremonotonousappearance.
Thus,forsemanticseamcarving,knowledgeoftheexactpositionsoftherelevant
WBCsiscrucial.Therefore,toobtainthepositionofWBCs,amodifiedvisualatten-
tionmodelisused,asproposedinourpreviousresearch[14].
Theoriginalsaliency-basedvisualattentionmodelwasproposedbyIttietal.[15],
andusescolor,luminance,andorientation.Themostsalientareasarethenselected
basedonawinner-take-allcompetitionmap.However,inthisstudy,asaliencymapis
usedfortheinitialdetectionofAttentionWindowsbasedonaweightedlinearcombi-
nationofacolormap,saturationmap,andorientationmapasshowninFig.4.
Toproducethecolormap,thisstudyusesaCIELabcolormodel,whereeacha*and
b*imageisdown-sampledtohalfthesizeoftheoriginalimage.Differentsizedfilters
s
Î
{
1
×
1
,13×13}
arethenappliedtothedown-sampled
a
and
b
images.Thefilters
estimatethecenter-surrounddifferencebetweenthecenterpointandthesurrounding
pointswithinthefilterscale
s
usingtwocolors
c
Î
{a,b}
,andthisdifferenceyieldsthe
featuremap,
C(c,s)
.Thesizeofthefiltersistypicallychosenbasedonthenumberof
availableobservations.Inthisstudy,thesizeof
s
wassetat11×11and15×15based
onseveralexperimentsusingan800×600imagesize.Hence,thefiltersizecanbe
changedaccordingtotheimagesize,withahalfvaluewhenusinga400×300image
sizeandviceversa.InEq.(1),thenormalizedcolordifferencemap
C
isestimated
from
C(c,s)
s.
1C
=
(
∑∑
C
(
c
,
s
))
(1)
4
c

{
a
,
b
}
s

{11
×
11,13
×
13}
Inparallelwiththecolorfeaturemap,theorientationfeaturemapisproducedusing
asimplewavelettransform.Afteraone-levelwavelettransform,horizontally(LH),
vertically(HL),anddiagonally(HH)orientatedsub-imagesareobtainedfromthe

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Figure4
Flowdiagramofsaliencymap
:(a)sourceimage,(b)orientationmap,(c)colormap,(d)
saturationmap,and(e)saliencymap.
waveletsubbands.Theorientationfeaturemap,
O(c,s)
isthenproducedfromthethree
sub-images
c
Î
{HH,HL,LH}
andtwofilters
s
Î
{11×11,13×13}
usingthesame
methodasforthecolorfeaturemap.InEq.(2),thenormalizedorientationdifference
map
O
isestimatedfrom
O(c,s)
s.
1O
=
(
∑∑
O
(
c
,
s
))
(2)
6
c

{
HH
,
HL
,
LH
}
s

{11
×
11,13
×
13}
Thenormalizedsaturationfeaturemap
S
isprocessedinasimilarwaytothecolor
mapusingasaturationfeaturemap,
S(c,s)
fromHIScolorspaceandtwofilters
s
Î
{
1
×
1
,13×13}
basedonthefollowingEq.
1S
=
(

S
(
c
,
s
))
(3)
2c

{
s
}
s

{11
×
11,13
×
13}
Afterthethreefeaturemapsareproduced,theyarecombinedintoasaliencymap.
However,sincecellimageshaveahighercontrastforcolorthanfororientationand
saturation,asdistinctfromnaturescenes,differentweightsneedtobeappliedtoeach
featuremapwhentheyarecombinedintoasaliencymap.Inthepresentstudy,the
mostaccurateAttentionWindowswereproducedwhenthecolorweightwas0.6,the
orientationweightwas0.2,andthesaturationweightwas0.2.Finally,thethreefeature
mapsarenormalizedandsummedintoasinglesaliencymapusingtheirweightsand
Eq.(4).Aftergeneratingthesaliencymap
C
m
,itisup-sampledtotheoriginalsize.
C
m
=
w
1

C
(
x
,
y
)
+
w
2

L
(
x
,
y
)
+
w
3

O
(
x
,
y
)
(4)

Ko
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2010,
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Saliencystrengthmapgeneration
Basedonthesaliencymap,AttentionWindows(AWs)arethendetectedtoremove
uselessregionsfromtheimage,suchasredbloodcellsandbackground,thereby
improvingthequalityoftheresizedimage.Todeterminetheproperlocationofthe
AWs,optimalthresholding[16]ofthesaliencymapisperformedfirst(Fig.5-(c)).This
thresholdingmethodisknowntoproducethebestperformancewhenanimageonly
containstwoprincipalregions(e.g.objectsandbackground)andthedistributionof
thegray-levelvaluesineachregionfollowsaGaussiandistribution[16].Morphological
closingisthenperformedtofilltheholesinthenucleioftheWBCs(Fig.5-(d)).Using
theresultingbinaryimage,regionlabelingisperformedandsmallregionsareconsid-
eredasnoiseandremoved(Fig.5-(e)).Atthesametime,theinitialAWpositions
(x,y)foreachremainingregionareestimatedbyX-Yprojection.Also,sinceWBCs
tendtohavearoundshape,theellipticalshapesoftheAWs(EAWs)arere-estimated
usingacentroidandtheradiusoftheinitialAW(Fig.5-(f)).Thereafter,adistance
transformusingtheEuclideandistanceisperformedtoboosttheintensitydifference
betweenthecentralandboundaryregions,termedthestrengthoftheEAW(
EAW
S
).
AsshowninFig.5-(g),theintensitystrengthoftheboundaryislowerthanthatofthe
centralregionineachnucleus.
ThesixstepsforextractingtheAWandestimatingthe
EAW
S
areasfollows:

tSep1:
Otsu[17]

soptimalthreshold
t
op
isappliedtothesaliencymap(
C
m
).
I
(
x
,
y
)
=⎧⎨
1
ifC
m
(
x
,
y
)
>
t
op
0
otherwise

⎩(5)

Figure5
SaliencystrengthusingEAWsandenergymap
:(a)originalimage(b)saliencymap,(c)
optimalthresholding,(d)morphologicalopening,(e)removalofsmallregions,(f)EAWs,(g)strengthof
EAWs,(h)SaliencyStrengthMap,(i)finalenergymapof(h),and(j)resizedimage.

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tSSetpe2p:3:Morphologicalopeningisperformedtofillanyholesinthecellnuclei.
Afterregionlabeling,smallregionsareremovedifthesizeofaregionis
belowapredefinedminimumthreshold(3%ofallimagepixels).Thispredefined
minimumthresholdwasdeterminedbyanalyzingtheminimumcellregionfrom
wholetrainingcellregions.
Step4:
TheinitialpositionoftheAWineachregionisestimatedusinganX-Y
projection.
Step5:
TheellipticalAWs(EAW)arere-estimatedusingthecentroidandradiusof
theinitialAW.
Step6:
AdistancetransformisperformedandthestrengthoftheEAW(
EAW
S
)
estimated.

ThesaliencymapandstrengthoftheEAWsarethensummedintoasinglesaliency
strengthmap(
S
M
)andnormalizedinto0~255,asshowninFig.5-(h).
1SSM
(
x
,
y
)
=
g
(

[
C
m
(
x
,
y
)
+
EAW
S
(
x
,
y
)])
(6)
2wheregrepresentsaGaussiansmoothingoperatortoreduceminornoise.
Finally,theresizedimagebasedontheSSManditsenergymapisshowninFig.5-(j).
Seamremovalusingenergymapbasedonsaliencystrengthmap
Aseamisamonotonicandconnectedpathofpixelsproceedingfromthetopofan
imagetothebottom,orfromlefttoright.Thus,whenaseamisremovedfroman
image,theimagesizeisreducedbyoneineitherthehorizontalorverticaldimension.
Likewise,seamcarvingusesanenergyfunctiondefinedbasedonthepixelstosucces-
sivelyremovetheminimumenergypathsfromanimage[17].Yet,asshownin6-(b),an
energymapusingonlythegradientmagnitudeintroducesvisualartifacts,regardlessof
theimportanceofthecells.WhereastheproposedenergymapsinFigs.5-(i)and6-(c)
showonlythehighestenergy,indicatingtheexistenceofWBCswithoutvisualartifacts.
Seamcarvingusestwotypesofenergyremovalstrategy:backwardandforward.
Backwardenergystrategiesarebasedonevaluatingtheenergy,yettheyintroduce
visualartifactsduetotheirseamremovalstrategy.Theseamscontainingthelowest
energyareremovedoneafteranother,however,theenergyinsertedintothenewedges
createdbypreviouslynon-adjacentpixelsthatbecomenewneighborsisignoredaftera

Figure6
Energymapcomparison
:(a)originalimage(b)energymapusingonlygradientmagnitude,
and(c)energymapusingsaliencystrengthofSSM.

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seamisremoved.Thus,toreducethesevisualartifacts,forwardenergystrategies[17]
substituteanenergyevaluationthatcalculatesthreepossibleseamstepcostsand
definestheminimalamountofenergyinsertedbytheremovalofaseam.
Intheseamremovalprocedureusedinthisstudy,thesaliencystrengthoftheSSM
isusedtocorrespondtothecomputingenergyforeachpixel.Computingthecostof
thesaliencystrengththenproducesthesameresultastheforwardenergystrategyof
Rubinsteinetal.[17].Asdistinctfrombackwardenergystrategies,threepossiblecases
arethencalculatedtoremoveseamswithforwardenergystrategiesusingthepixel
valuesof
S
M(i,j)
andthefollowingformula:
(
a
)
C
L
(
i
,
j
)
=
|
SSM
(
i
,
j
+
1)

SSM
(
i
,
j

1)|
+
|
SSM
(
i

1,
j
)

SSM
(
i
,
j

1)|
(
b
)
C
U
(
i
,
j
)
=
|
SSM
(
i
,
j
+
1)

SSM
(
i
,
j

1)|
(7)
(
c
)
C
R
(
i
,
j
)
=
|
SSM
(
i
,
j
+
1)

S
SM
(
i
,
j

1)|
+
|
SSM
(
i

1,
j
)

SSM
(
i
,
j
+
1)|
where
SSM
(
i
,
j
-1)isthenewpixelthatisreplacedafterremoving
S
M
(
i
,
j
),
SSM
(
i
,
j
+1)and
SSM
(
i
-1,
j
)arethenewrightandupperneighbors,respectively,and
C
L
,
C
U
,
and
CR
representthecostsofthethreepossibleverticalseams.
AcostmatrixMisthencreatedtocomputetheseams.

M
(
i

1,
j

1)
+
C
L
(
i
,
j
)
⎪M
(
i
,
j
)
=
P
(
i
,
j
)
+
min

M
(
i

1,
j
)
+
C
U
(
i
,
j
)
(8)
⎩⎪
M
(
i

1,
j
+
1)
+
C
R
(
i
,
j
)
where
P
(
i
,
j
)isthegradientvalueobtainedfrom
S
M(i,j)
,
M
(
i
-1,
j
-1)istheleftupper
neighbor,and
C
L
isitscost.Thecostofthecorrespondingupper
M
(
i
-1,
j
)neighbors
C
U
andrightupper
M
(
i
-1,
j
+1)neighbors
C
R
arecomputedinthesamemannerto
determinetheminimumenergyofthenewsaliencystrengthafterremoving
S
M
(
i
,
j
).
Oncethecostmatrixisconstructed,
M
(
i
,
j
)inarandompositionrepresentsapixel
(
i
,
j
)inapathcrossingtheimagefromtoptobottom,andisconnectedtootheradja-
centpixelscontainingtheminimalenergyaccordingtothesaliencystrength.Conse-
quently,theresizingisperformedbyiterativelycreatingacostmatrixafterblending
thegapsarisingfromseamremoval.
Fig.7showsacomparisonoftheresultsofseamcarvingwhenusingagradient-
basedenergymapandtheproposedenergymap.Whilethegradient-basedenergymap

Figure7
Comparisonofseamcarvingresults
:(b)resizedimageusingoriginalseamcarvingand(c)
resizedimageusingproposedmethod.

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distortstheWBCnucleiduetoaharshreductionoftheimagesize,theproposed
methodisabletopreservetheoriginalshapeoftheWBCnuclei.
Resultsanddiscussion
TheexperimentaltestsusedcolorperipheralbloodimagescollectedattheSeverance
Hospital,YonseiUniversity.The8testimageswerebasedonaslideofaperipheral
bloodsmearandtakenusingamicroscope,charge-coupleddevice(CCD)camera,and
24-bitdigitizerwithan800×600imagesize.
Asthereisnospecificmethodforevaluatingtheperformanceofimageresizing,the
PeakSignal-to-NoiseRatio(PSNR)wasusedfirsttoevaluatethecontentpreservation
oftheproposedmethod.Plus,theRatioofDistortion(ROD)wasappliedtoevaluate
thegeometricdistortionoftheresizedimages.Notethat,thesizeofthesourceimages
was800×600andthetargetsizewasautomaticallydeterminedaccordingtothesize
oftheEAWstoincludeallthenucleiwithoutdistortion.
PSNR(PeakSignal-to-NoiseRatio)comparison
Anexperimentalcomparisonofseamcarvingisgenerallyverydifficultasthereareno
standardcriteriaforperformancetests.Thus,tovalidatetheeffectivenessofthepro-
posedapproach,thisstudyusedthePeakSignal-to-NoiseRatio(PSNR).
ThePSNRoriginallycomesfromelectronicsandrepresentstheratiobetweenthe
maximumpossiblepowerofasignalandthepowerofthenoisethataffectsthefidelity
ofitsrepresentation.However,inthefieldofimageprocessing,thePSNRisusedto
measureofthequalityofanimageoritscompression.Inthecaseofimageprocessing,
themaximumvalue(
MAX
)ofintensitylevel255isusedinsteadofthemaximumpos-
siblepower.InEq.(9),theMSErepresentsthemeansquareerrorbetweentheoriginal
image
I(i,j)
andthetargetimage
K(i,j)
.
m

1
n

1
MSE
=
1
∑∑
||
I
(
i
,
j
)

K
(
i
,
j
)||
2
(9)
mn
i
=
0
j
=
0
Where
m
and
n
representwidthandheightoftheimage,respectively.
ThePSNRisthenestimatedbycomputingtheratioofthemaximumvaluetothe
MSEusingEq.(10).

MAX
2
⎞⎛
MAX

PSNR
=
10

log
10

I
⎟=
20

log
10

I

(10)

MSE
⎟⎝
MSE

⎝⎠Fortheperformancetest,gradient-basedseamcarvingandtheproposedmethod
wereappliedtothesourceimagestocreatetargetimages.TheWBCnucleiwerethen
croppedmanuallyfromeachimagewithagraphictoolandusedforthePSNR
comparison.
Fig.8showstheimagequalityresultswhenusingaJPEGcompressionmethod,
imageresizingwithseamcarving,andimageresizingwiththeproposedmethod.
Clearly,theseamcarvingbasedonthegradientenergyproducedWBCnucleiwith
distortedshapesandsizes,whereastheJPEGcompressionandtheproposedmethod
preservedtheshapesandsizesoftheWBCnuclei.

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