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dc.contributor.authorChristodoulou, Christodoulos I.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorPantzaris, Marios C.en
dc.contributor.authorTegos, Thomas J.en
dc.contributor.authorNicolaïdes, Andrew N.en
dc.contributor.authorElatrozy, Tarek S.en
dc.contributor.authorSabetai, Michaelen
dc.contributor.authorDhanjil, S.en
dc.creatorChristodoulou, Christodoulos I.en
dc.creatorPattichis, Constantinos S.en
dc.creatorPantzaris, Marios C.en
dc.creatorTegos, Thomas J.en
dc.creatorNicolaïdes, Andrew N.en
dc.creatorElatrozy, Tarek S.en
dc.creatorSabetai, Michaelen
dc.creatorDhanjil, S.en
dc.description.abstractThe objective of this work was to develop a computer aided system which will facilitate the automated characterization of carotid plaques recorded from high resolution ultrasound images for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. The plaques were classified into (i) symptomatic or (ii) asymptomatic. Ten different texture feature sets were extracted from the segmented plaque image using the following algorithms: fast order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. Although the statistics of all features extracted for the two classes indicated a high degree of overlap, a classification of the plaques was possible using the unsupervised self-organizing feature map (SOFM) classifier and combining techniques. The classification results of the different feature sets were combined using (i) majority voting and (ii) weighted averaging based on a confidence measure derived from the SOFM. Combining the classification results of the ten different feature sets improved significantly the classification results obtained by the individual feature sets, reaching an average diagnostic yield of 75%.en
dc.sourceProceedings of the International Joint Conference on Neural Networksen
dc.sourceInternational Joint Conference on Neural Networks (IJCNN'99)en
dc.subjectStatistical methodsen
dc.subjectFeature extractionen
dc.subjectLearning algorithmsen
dc.subjectNeural networksen
dc.subjectMatrix algebraen
dc.subjectUltrasonic imagingen
dc.subjectImage analysisen
dc.subjectMedical imagingen
dc.subjectLearning systemsen
dc.subjectComputer aided analysisen
dc.subjectCardiovascular systemen
dc.subjectPattern recognition systemsen
dc.subjectSelf-organizing feature maps (SOFM)en
dc.titleMulti feature texture analysis for the classification of carotid plaquesen
dc.description.endingpage3596 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Cited By :15</p>en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidPantzaris, Marios C. [0000-0003-2937-384X]

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