Computerized analysis of digital subtraction angiography: a tool for quantitative in-vivo vascular imaging

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dc.contributor.authorKagadis, G. C.en
dc.contributor.authorSpyridonos, P.en
dc.contributor.authorKarnabatidis, D.en
dc.contributor.authorDiamantopoulos, A.en
dc.contributor.authorAthanasiadis, E.en
dc.contributor.authorDaskalakis, A.en
dc.contributor.authorKatsanos, K.en
dc.contributor.authorCavouras, D.en
dc.contributor.authorMihailidis, D.en
dc.contributor.authorSiablis, D.en
dc.contributor.authorNikiforidis, G. C.en
dc.date.accessioned2015-11-24T19:28:01Z
dc.date.available2015-11-24T19:28:01Z
dc.identifier.issn1618-727X-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/22860
dc.rightsDefault Licence-
dc.subjectAngiography, Digital Subtraction/*methodsen
dc.subjectAnimalsen
dc.subjectContrast Media/diagnostic useen
dc.subjectDisease Models, Animalen
dc.subjectFalse Negative Reactionsen
dc.subjectFalse Positive Reactionsen
dc.subjectHindlimb/blood supply/radiographyen
dc.subjectHumansen
dc.subjectImage Processing, Computer-Assisted/*methodsen
dc.subjectNeovascularization, Pathologic/radiographyen
dc.subjectNormal Distributionen
dc.subjectObserver Variationen
dc.subjectROC Curveen
dc.subjectRabbitsen
dc.subjectRadiographic Image Enhancement/methodsen
dc.subjectReproducibility of Resultsen
dc.subjectSensitivity and Specificityen
dc.subjectSoftwareen
dc.subjectSubclavian Artery/radiographyen
dc.subjectTriiodobenzoic Acids/diagnostic useen
dc.titleComputerized analysis of digital subtraction angiography: a tool for quantitative in-vivo vascular imagingen
heal.abstractThe purpose of our study was to develop a user-independent computerized tool for the automated segmentation and quantitative assessment of in vivo-acquired digital subtraction angiography (DSA) images. Vessel enhancement was accomplished based on the concept of image structural tensor. The developed software was tested on a series of DSA images acquired from one animal and two human angiogenesis models. Its performance was evaluated against manually segmented images. A receiver's operating characteristic curve was obtained for every image with regard to the different percentages of the image histogram. The area under the mean curve was 0.89 for the experimental angiogenesis model and 0.76 and 0.86 for the two clinical angiogenesis models. The coordinates of the operating point were 8.3% false positive rate and 92.8% true positive rate for the experimental model. Correspondingly for clinical angiogenesis models, the coordinates were 8.6% false positive rate and 89.2% true positive rate and 9.8% false positive rate and 93.8% true positive rate, respectively. A new user-friendly tool for the analysis of vascular networks in DSA images was developed that can be easily used in either experimental or clinical studies. Its main characteristics are robustness and fast and automatic execution.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primary10.1007/s10278-007-9047-2-
heal.identifier.secondaryhttp://www.ncbi.nlm.nih.gov/pubmed/17674102-
heal.identifier.secondaryhttp://www.springerlink.com/content/4m78w26860g17935/fulltext.pdf-
heal.journalNameJ Digit Imagingen
heal.journalTypepeer-reviewed-
heal.languageen-
heal.publicationDate2008-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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