Публікація:
Development of method of matched morphological filtering of biomedical signals and images

dc.contributor.authorPovoroznyuk, A. I.en
dc.contributor.authorFilatova, A. E.en
dc.contributor.authorZakovorotniy, A. Yu.en
dc.contributor.authorShehna, Kh.en
dc.date.accessioned2020-04-03T14:49:51Z
dc.date.available2020-04-03T14:49:51Z
dc.date.issued2019
dc.description.abstractFormalized approach to the analysis of biomedical signals and images with locally concentrated features is developed on the basis of matched morphological filtering taking into account the useful signal models that allowed generalizing the existing methods of digital processing and analysis of biomedical signals and images with locally concentrated features. The proposed matched morphological filter has been adapted to solve such problems as localization of the searched structural elements on biomedical signals with locally concentrated features, estimation of the irregular background aimed at the visualization quality improving of biological objects on X-ray biomedical images, pathologic structures selection on mammogram. The efficiency of the proposed methods of matched morphological filtration of biomedical signals and images with locally concentrated features is proved by experiments.en
dc.identifier.citationDevelopment of method of matched morphological filtering of biomedical signals and images / A. I. Povoroznyuk [et al.] // Automatic control and computer sciences. – 2019. – Vol. 53, No. 3. – P. 253-262.en
dc.identifier.urihttps://repository.kpi.kharkov.ua/handle/KhPI-Press/45424
dc.language.isoen
dc.publisherAllerton Pressen
dc.subjectbiomedical signalen
dc.subjectbiomedical imageen
dc.subjectlocally concentrated featuresen
dc.subjectmatched morphologic filteren
dc.subjectuseful signal modelen
dc.titleDevelopment of method of matched morphological filtering of biomedical signals and imagesen
dc.typeArticleen
dspace.entity.typePublication
relation.isAuthorOfPublicationae651cb9-5fd6-465c-ad56-3d654b28257d
relation.isAuthorOfPublication.latestForDiscoveryae651cb9-5fd6-465c-ad56-3d654b28257d

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