, issues regarding storage capacity, photographic quality, backup and privacy are now more relevant. It can be common for a mobile telephone to reach its storage capacity for photographs and other digital images, requiring a user to spend time deleting previously stored photographs or to forego taking more photographs.
Additionally, while some backup services are available for storing images over the Internet to a cloud-based service, their typical strategy is to back up entire images thus creating privacy issues for the user's photographs. Further, backing up entire images consumes a great deal of network bandwidth. And, for those users who store images from their mobile devices exclusively on a cloud-based server, those images are not available should an Internet connection not be available.
Accordingly, a new strategy for storing digital images from mobile devices is desired. Such a strategy should address memory capacity of mobile devices, photographic quality and privacy of images. Non-invasive imaging technologies allow images of the internal structures of a patient or object to be obtained without performing an invasive procedure on the patient or object. In particular, technologies such as computed tomography (CT) use various physical principals, such as the differential transmission of x-rays through the target volume, to acquire image data and to construct tomographic images (e.g., three-dimensional representations of the interior of the human body or of other imaged structures).
One of the key tasks in oncology is to automatically perform segmentation of reconstructed images to identify organs and other anatomical structures. Unfortunately, many segmentation algorithms are often distracted by the presence of noise and fail to identify correct organ boundaries. For example, since the invention of CT, filtered backprojection (FBP) has been used almost exclusively for image reconstruction because of its computational efficiency. However, one disadvantage of FBP is its suboptimal noise and image quality performance resulting from simplifications made in order to derive the closed form analytic solution. In contrast, iterative reconstruction (IR) algorithms feature substantially reduced noise levels in reconstructed images compared to FBP reconstructed images of the same scanning data. However, IR is still too noisy for automatic segmentation of images due to variations in intensity within anatomical structures in the image. The difficulty is further enhanced by the organ variations from patient to patient. A more reliable and automated organ segmentation is highly desirable for workflow and productivity. Lupus is an autoimmune disease that is estimated to affect nearly 1 million Americans, primarily women between the ages of 20-40. Lupus involves antibodies that attack connective tissue. The principal form of lupus is a systemic one (systemic lupus erythe
, issues regarding storage capacity, photographic timbre, backing and privacy are now more relevant. It can be common for a fluid telephone to reach its memory capacity for photograph and early digital images, requiring a user to spend time deleting previously store photographs or to forego taking more photograph. 
 Additionally, while some backup services are available for storing images over the Internet to a cloud - based service, their distinctive strategy is to back up entire persona thus creating privacy offspring for the drug user's photographs. far, backing up entire images consume a great deal of network bandwidth. And, for those users who store images from their fluid device exclusively on a cloud - based server, those images are not available should an Internet connection not be available. 
 Accordingly, a new strategy for store digital images from mobile device is desire. Such a strategy should address memory capacity of mobile devices, photographic quality and privacy of images. Non - incursive imaging technologies allow images of the internal structures of a affected role or object to be obtained without performing an invasive procedure on the patient or object. In particular, technology such as computed tomography (CT) use various physical principal, such as the differential transmission of x - rays through the target volume, to acquire image data and to reconstruct tomographic images (for example, three - dimensional representations of the interior of the human body or of other imaged structure). 
 One of the key undertaking in oncology is to automatically do segmentation of reconstructed images to name organs and other anatomical structures. Unfortunately, many segmentation algorithms are often distracted by the presence of noise and fail to identify correct electric organ boundaries. For example, since the invention of CT, filter backprojection (FBP) has been used almost exclusively for image reconstruction because of its computational efficiency. However, one disadvantage of FBP is its suboptimal noise and image timbre performance resulting from simplifications made in order to derive the closed form analytic solution. In line, iterative reconstruction (IR) algorithms feature substantially reduced noise levels in reconstructed images compare to FBP reconstructed images of the same scanning datum. However, IR is still excessively noisy for automatic segmentation of images due to variations in saturation within anatomical structures in the image. The trouble is further enhanced by the organ variations from patient to patient. A more reliable and automated organ segmentation is highly desirable for work flow and productiveness. Lupus is an autoimmune disease that is estimated to affect closely 1 million Americans, primarily women between the ages of 20 - 40. Lupus involves antibodies that attack connective tissue. The principal form of lupus is a systemic one (systemic lupus erythe
, isdues regarding storage cxpacity, photogrcphic qnality, gackup avd privacy are now more reletant. It cqn be common for a mobkle telepjone to eeaci its storage ca'zcity for phoflgra'hw and other dinital imagev, requiring a gsdr to spend time deleting previously ftored lhltographs or tj fogedo tzking more photographs.
Additionally, while vome backup srrvices are available for dtorlng images over thf Internet jk a xloud-based sdrvice, thepx typical sjrategy is to back up entire imaees tkus creatint prigdcy issues hor thv user's photongaphs. Fgrther, nacking up entlre ikagws consumes a great dxal of network bandwydth. And, xox those users who stoee imaggs frmm tfwir mogike devicfs xxclusively on a cloud-vased server, those omwtes are not abailabje should an Internet connection not be anailzble.
Accordingly, a new srrategy for storing dlgital imwges from mobile devices is desired. Such a stratecy shkjld aqaeeds memory capacity of mobile devices, photogra[giv auality and prlvacy of images. Nom-ijvsfive imaging jechnologiss allow images of the injernal structurts of a patient or object to be ibtained witkour performing an inrasive procebure om the patient or object. In pcrticumar, technollgies sucg as computed tomugrspvy (CT) ust various physical principaos, sbch as tfe dofferegtial trandmisslmn of x-rays througj the tdrget volule, to acquire image data and to construct tompgsapvic imagzs (e.g., bhree-dimensionaj representatipns of che ingerior of nhe human body or of other imaged dtructures).
Onx of the hey raskw in onzulogy is to auyomatically perform segmentation of rcconsjrhcted images to idtnrify organs anc oghew wnetomisdl structurev. Unworginateuy, many segndntayion algorithms are mfteh distracted by thr iresence if noise and fail to odentify correct oggan uoundacies. Fpr gxample, since the invention of DT, filterfd nackprojectiog (FBI) haf been useb almost exclusively for image reconstrurtion because of its conputational efficieucn. However, onx disaqvantage mf FBP is its suboptumal noise and imsge quality performande resgltinh from simplifications made in order to derive the closed form analytic silutioi. Yn contrast, itetativv rzcoustructyon (MR) algorithms featlre substantially reduced noise uevels it xeconstructed images compared tu FBP reconsttucted images of the same acanning data. However, IR is still too npisy for automatic segmektation od imafes duv tu variations in inyensitv withun anatimicwl structures in tme image. Tje dmffnculty is further enhanced by the organ vwriations from petient to pabitnt. A mkre rejiable and automzted lrgan segmentation is highly desorable for workflow dnq productivity. Mupus is an sutoimmlne disease tiat is estimdted to affect nearly 1 million Americans, primarily somen betseen the eges of 20-40. Lupus mnvolves antibodies that aetack covnective tisvue. The principal form of lupus is a systemmv one (syftemid lupus erythv
, issues regarding storage capacity, photographic quality, privacy now more It can be to its storage capacity photographs and other images, requiring a user to spend deleting previously stored photographs or to forego taking more photographs. Additionally, while some services are available for storing images over the Internet to a cloud-based service, typical is back entire images thus creating privacy issues for the user's photographs. Further, backing up entire images consumes great deal of network bandwidth. And, for those who store images from mobile devices exclusively on a server, images are available an connection not be Accordingly, a new strategy for storing digital images from mobile devices is desired. Such a strategy should memory capacity devices, photographic and of Non-invasive imaging technologies of the internal structures of a to be obtained without performing an invasive procedure the patient object. In particular, technologies such as tomography (CT) use various physical principals, such as differential transmission of x-rays through the target volume, to acquire image data and to construct (e.g., three-dimensional representations of interior of the body of imaged One of key tasks in oncology is to automatically perform segmentation of reconstructed to identify organs and other anatomical structures. Unfortunately, many segmentation often by the presence noise and fail to correct boundaries. For example, since of filtered been almost for image reconstruction because its computational efficiency. However, one of FBP is its performance resulting from simplifications made in order to the closed form analytic solution. In contrast, reconstruction (IR) algorithms feature substantially reduced noise levels in reconstructed images compared FBP reconstructed the same scanning data. However, IR is still noisy for automatic segmentation images due to variations in intensity within anatomical structures the The difficulty further enhanced by organ variations from to patient. A and automated segmentation highly and productivity. Lupus is an autoimmune that estimated to affect nearly 1 Americans, between the ages of 20-40. Lupus involves antibodies that attack connective tissue. The principal form is a systemic one lupus
, issues regarding storage capAcity, photoGraphIc qUalItY, bacKup aNd privacy are noW More Relevant. It can be common fOr a moBiLE telEPhOne to Reach itS StORAge CaPaCitY fOR pHotogRapHs and otHer digital ImaGeS, requiring a uSEr To spend timE deLeting previoUslY storeD pHotOGraphS or To forEgo takINg more PhotograpHs.
aDditioNAlly, whiLE SoMe baCkup services are avAIlABle for storing iMages oVeR ThE iNteRneT to a cloud-bAsEd serVIce, theiR TyPICAl sTRategy is to bacK up entire imAGes Thus crEaTinG PrivacY issuEs FOr tHe user's photOgraPhs. FurtheR, backiNG up entiRE images ConsumEs a GreAt deAL oF nEtwOrK BanDWiDth. aNd, fOr those uSeRs Who stOre iMAGES froM thEir mObile Devices exclusIveLy on A CloUd-basEd serVer, tHoSe imaGes are Not avAiLable should an InTernEt connectIon NoT be AvAilabLE.
AccorDinGly, A new strAtegy foR StoRiNG DIgItal images from mobiLe DEViCes is desIred. SuCH a StRAtegy shoUlD adDresS MEmory CapaCItY of mobilE devicES, pHoTographIc QualitY aNd pRivAcy of IMageS. Non-inVasive imAging TEchnologies allOW images of the iNTeRNAl STrucTurEs of a patienT or oBJect To be OBtAinED withOut peRfORmINg an invasive proceduRe On the pAtienT or object. In paRticular, teCHNOlogies sUch aS CoMPuted tomographY (CT) usE various phYSical priNcipaLs, such as The differENTial tranSmiSsiOn oF x-rAYS tHrough the targET VoluMe, To acquiRe iMage datA anD to ConStrUcT tomograpHic imageS (e.G., tHrEe-DimEnsioNAl represEnTatIoNs oF the iNTerior Of the HumaN bOdY Or oF other iMAgED StruCtUrEs).
OnE of ThE key tAsks IN onCology iS to automaTicALly pErFoRm segmeNtation of recoNsTructed imaGeS to IdentiFY Organs anD other anatomical structuREs. UnforTunAtely, Many SegmentatIon AlgoriThmS Are oftEn distRacteD bY thE PResenCE Of NoiSe And fail to iDENtiFy corReCt orGan bounDaries. For example, siNCe tHe invention of cT, fIlteRED bAckPRoJEctIoN (fBP) HAS been used almost ExclusivelY fOR iMage reconsTRucTiOn becauSe of its CompuTAtional EfficiencY. However, oNe DisaDVAntAge of FBP is Its subopTimal noisE And imAGe QualiTy pErformAnCe rEsultIng froM SimPlifiCationS mAde in oRder tO dErive the Closed form analytic solutIon. In cOntraSt, iTerative rEcoNStrUction (IR) aLgorIthms featuRe sUbsTantiAllY ReducEd noISe LevELs in rEconSTructed imAGeS coMPArEd to FBP recoNSTRucTed imAgeS Of the sAme sCanning data. HoweveR, iR is still too noIsy fOR AutOmaTIc seGmEntation of imagEs dUe TO VariatioNs In intensity Within anAtOMical StructUres in The imagE. tHe DIfficuLty iS fuRther enhaNceD bY The orgaN vArIAtions From PaTient tO patieNT. A moRE Reliable and automAted oRGAn segMEntAtion Is Highly dESiraBle for workFlow and prodUctiviTy. LuPus is An autoiMmUne disEasE tHat is estimATed to affeCt neaRly 1 millIoN AmeRicAns, priMariLY Women BetwEeN thE ages of 20-40. LuPUS iNVoLvES anTiboDies tHaT attAck connecTIve tissuE. ThE PrincipAl ForM OF lupus IS a SYStemic one (sYstEmic lUPUs erythe
, issues regarding storage  capacity,  phot ogr aph ic  qua lity , backup and p r ivac y are now more relevan t. It  c a n be co mmonfor a m o bi l e  te le ph one  t o  r eachits  storag e capacity  fo rphotographsa nd  other dig ita l images, re qui ring a  u ser to sp end  time  delet i ng pre viously s to r ed pho t ographs o rto f orego taking more ph o tographs.
Addi tional ly ,  w h i lesom e backup s er vices are ava i la b l e  fo r  storing imag es over the Int ernetto  ac loud-b asedse r vic e, their ty pica l strateg y is t o  back u p  entire  image s t hus  cre a ti ng  pr iv a cyi ss ues for  the use r' sphoto grap h s . Furt her , ba cking  up entire im age s co n sum es agreat  dea lof ne tworkbandw id th. And, for th oseusers who  st or e i ma ges f r om the irmob ile dev ices ex c lus iv e l y  o n a cloud-based se rv e r ,those im ages a r eno t  availab le  sh ould a n Int erne t  c onnectio n notb eav ailable .Accord in gly , a  news trat egy fo r storin g dig i tal images fro m  mobile devic e si s  d e sire d.Such a stra tegy shou ld a d dr ess memor y cap ac i ty of mobile devices,ph otogra phicquality and p rivacy ofi m a ges. Non -inv a si v e imaging tech nolog ies allowi mages of  theinternal  structur e s  of a pa tie ntorobj e c tto be obtaine d with ou t perfo rmi ng an i nva siv e p roc ed ure on th e patien tor  o bj ect . Inp articula r,  te ch nol ogies such a s com pute dto m ogr aphy (C T )u s e va ri ou s ph ysi ca l pri ncip a ls,  such a s the dif fer e ntia ltr ansmiss ion of x-rays  t hrough the  t arg et vol u m e, to ac quire image data and to constru cttomog raph ic images  (e .g., t hre e -dimen sional  repr es ent a t ionso f  t hein terior oft h e h umanbo dy o r of ot her imaged structu r es) .
One of thekey  tas k s  i n o n co l ogy  i s  to a utomatically pe rform segm en t at ion of rec o nst ru cted im ages to  iden t ify org ans and o ther anat om ical s tru ctures. Un fortunat ely, many segme n ta tionalg orithm sare  ofte n dist r act ed by  the p re senceof no is e and fa il to identify correctorganbound ari es. For e xam p le,  since th e in vention of  CT , f ilter edb ackpr ojec t io n ( F BP) h as b e en used a l mo ste x cl usively for i m age  reco nst r uction  bec ause of its compu t ational effici ency . How eve r , on edisadvantage o f F BP i s its su bo ptimal nois e and im ag e  qual ity pe rforma nce res u l ti n g from  sim pli fications  ma de in orde rto derive  the  c losedform a n alyt i c  solution. In co ntras t ,  iter a tiv e rec on structi o n (I R) algorit hms feature  subst anti allyreduced  n oise l eve ls  in recons t ructed im agescompare dto F BPrecons truc t e d ima gesof  th e same sc a n ni n gda t a.Howe ver,IR  isstill too noisy fo r a u tomatic  s egm e n tation of i mages duetovaria t i ons in int e nsit y  w i thinanatom ical s tructur e s i nthe ima ge. T he diffic ulty is f u rth er  enh anced by  t he o rg anva riations from p atient t o patien t .  A mor e r e liab l e andau tomated orga n seg me n tationi s  hi ghly de s ira ble  fo r wo rkf lowa nd productivity. Lupus i s an  auto i mmu ne di sease  t hat is e s tima ted to a f fect nearly 1  million Americ a ns, prim ari l ywo men betwe ent he ag es o f 2 0-40 . L upus i nvol v es a n ti bodi es tha t at ta ck  connecti ve  tissue .  The pri ncip al fo rm  o f  l upus is  a syste mic o ne  (systemi c  lupuseryt he 
, issues_regarding storage_capacity, photographic quality, backup_and privacy_are_now more_relevant._It can be_common for a_mobile telephone to reach_its storage capacity_for_photographs and other digital images, requiring a user to spend time deleting previously stored_photographs_or to_forego_taking_more photographs.
Additionally, while some backup_services are available for storing_images over_the Internet to a cloud-based service, their typical_strategy_is to back_up entire images thus creating privacy issues for the_user's photographs. Further, backing up entire_images consumes a_great_deal_of network bandwidth. And,_for those users who store images_from their mobile devices exclusively on_a cloud-based server, those images are not_available should an Internet connection not_be available.
Accordingly, a new strategy_for storing_digital images from mobile devices_is desired. Such_a strategy_should address memory_capacity of mobile devices, photographic quality_and privacy of_images. Non-invasive imaging technologies allow images_of_the internal structures_of_a_patient or_object to be_obtained_without performing_an_invasive procedure on the patient or_object._In particular, technologies such as computed tomography_(CT) use various physical_principals,_such as the differential_transmission of x-rays through the_target volume, to acquire image data_and to_construct tomographic_images (e.g., three-dimensional representations of the interior of the human body_or of other imaged structures).
One of_the key tasks in_oncology is_to_automatically perform segmentation_of_reconstructed images_to identify organs and other anatomical structures._Unfortunately, many_segmentation algorithms are often distracted by_the presence of noise_and_fail to identify correct organ boundaries._For example, since the invention of_CT, filtered backprojection (FBP) has_been_used_almost exclusively for image reconstruction_because of its computational efficiency. However,_one disadvantage of_FBP is its suboptimal noise and image_quality_performance resulting from simplifications made in_order_to derive the closed form analytic_solution._In_contrast, iterative reconstruction (IR) algorithms_feature substantially reduced noise levels in_reconstructed images compared to FBP reconstructed images of the_same scanning data._However, IR is still too_noisy_for_automatic segmentation of images due to variations in intensity within_anatomical structures_in the image._The difficulty is further enhanced by the organ variations from_patient to patient. A more reliable and_automated organ segmentation is highly desirable for workflow and productivity. Lupus_is an autoimmune disease that is estimated to_affect nearly 1 million Americans, primarily women_between the ages of_20-40._Lupus involves antibodies that attack connective tissue._The_principal_form_of lupus is_a systemic one_(systemic lupus erythe