Εμφάνιση απλής εγγραφής

dc.contributor.authorLeandrou, Stephanosen
dc.contributor.authorPetroudi, Stylianien
dc.contributor.authorKyriacou, Panicos A.en
dc.contributor.authorReyes-Aldasoro, C. C.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.editorKyriacou, Efthyvoulos C.en
dc.contributor.editorChristofides, Steliosen
dc.contributor.editorPattichis, Constantinos S.en
dc.creatorLeandrou, Stephanosen
dc.creatorPetroudi, Stylianien
dc.creatorKyriacou, Panicos A.en
dc.creatorReyes-Aldasoro, C. C.en
dc.creatorPattichis, Constantinos S.en
dc.date.accessioned2019-11-13T10:40:56Z
dc.date.available2019-11-13T10:40:56Z
dc.date.issued2016
dc.identifier.isbn978-3-319-32701-3
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54367
dc.description.abstractMedical image analysis and visualization, can contribute in quantitative and qualitative analysis of Magnetic Resonance Imaging (MRI) towards an earlier diagnosis of Alzheimer’s disease (AD). Moreover, the early detection of Mild Cognitive Impairment (MCI) has recently attracted a lot of attention. The main objective of this paper is to present a survey of recent key papers focused on the classification of MCI and AD and the prediction of conversion from MCI to AD using volume, shape and texture analysis. The most frequent anatomical features used in the assessment of AD, is the hippocampus, the cortex and the local concentration of grey matter. Shape analysis can identify the signs of early hippocampal atrophy, whereas volume analysis evaluates the structure as a whole. Shape analysis seems to be a more accurate technique both in classification of patients and in prognostic prediction. Compared to volume, shape and voxel based morphometry (VBM) techniques, texture analysis can be used to identify the microstructural changes before the larger-scale morphological characteristics which are detected by the other aforementioned techniques. We concluded that quantitative MRI measurements can be used as an in vivo surrogate for the classification of patients and furthermore, for the tracking the Alzheimer’s disease progression. © Springer International Publishing Switzerland 2016.en
dc.publisherSpringer Verlagen
dc.sourceIFMBE Proceedingsen
dc.source14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84968563048&doi=10.1007%2f978-3-319-32703-7_56&partnerID=40&md5=547152a4c6d3beb79b2185359edd3f93
dc.subjectForecastingen
dc.subjectDiagnosisen
dc.subjectMagnetic resonance imagingen
dc.subjectClassificationen
dc.subjectClassification (of information)en
dc.subjectBrainen
dc.subjectMedical imagingen
dc.subjectBiochemical engineeringen
dc.subjectMedical computingen
dc.subjectPredictionen
dc.subjectAlzheimeren
dc.subjectAlzheimer’s diseaseen
dc.subjectBrain volumeen
dc.subjectHippocampusen
dc.subjectMild Cognitive Impairmenten
dc.subjectMild cognitive impairmentsen
dc.subjectQuantitative MRIen
dc.subjectQuantitative mrisen
dc.subjectTemporal lobeen
dc.subjectTemporal lobesen
dc.titleAn overview of quantitative magnetic resonance imaging analysis studies in the assessment of alzheimer’s diseaseen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.identifier.doi10.1007/978-3-319-32703-7_56
dc.description.volume57
dc.description.startingpage281
dc.description.endingpage286
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors:en
dc.description.notesConference code: 172989en
dc.description.notesCited By :1</p>en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidKyriacou, Efthyvoulos C. [0000-0002-4589-519X]
dc.gnosis.orcid0000-0003-1271-8151
dc.gnosis.orcid0000-0002-4589-519X


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