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Contributions to lossless coding of medical images using minimum rate predictors

2015-12Conference paper Restricted access
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorJoao M. Santos
dc.contributor.authorGuarda, André
dc.contributor.authorM. M. Rodrigues, Nuno
dc.contributor.authorFaria, Sergio
dc.date.accessioned2025-06-30T16:01:44Z
dc.date.available2025-06-30T16:01:44Z
dc.date.issued2015-12
dc.descriptionArticle number - 7351340
dc.description.abstractMedical imaging compression is experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like MRI or CT. Furthermore, legal and diagnosis restrictions impose the use of lossless compression and data archival for several years. These facts create a demand for more efficient compression tools, used for archiving and communication. In this work, we first evaluate the performance of traditional medical image compression algorithms against that of recent state of the art lossless image encoders. We then propose a method to improve the Minimum Rate Predictors lossless encoder, by exploiting inter picture redundancy in volumetric anatomical images. Results show that the proposed method is more efficient than state of the art encoders, such as HEVC, by about 28.8%, and achieves a gain of up to 57.8% in compression ratio when compared with traditional methods.eng
dc.description.sponsorshipThis work is part of UDICMI project funded by CENTRO-07-ST24-FEDER-002022 of QREN and MEDICOMP project funded by UID/EEA/50008/2013.
dc.identifier.citationJ. M. Santos, A. F. R. Guarda, N. M. M. Rodrigues and S. M. M. Faria, "Contributions to lossless coding of medical images using minimum rate predictors," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 2015, pp. 2935-2939, doi: 10.1109/ICIP.2015.7351340.
dc.identifier.doi10.1109/icip.2015.7351340
dc.identifier.urihttp://hdl.handle.net/10400.8/13474
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationInstituto de Telecomunicações
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/7351340
dc.relation.ispartof2015 IEEE International Conference on Image Processing (ICIP)
dc.rights.uriN/A
dc.subjectMedical imaging compression is experiencing a growth in terms of usage and image resolution
dc.subjectnamely in diagnostics systems that require a large set of images
dc.subjectlike MRI or CT. Furthermore
dc.subjectlegal and diagnosis restrictions impose the use of lossless compression and data archival for several years. These facts create a demand for more efficient compression tools
dc.subjectused for archiving and communication. In this work
dc.subjectwe first evaluate the performance of traditional medical image compression algorithms against that of recent state of the art lossless image encoders. We then propose a method to improve the Minimum Rate Predictors lossless encoder
dc.subjectby exploiting inter picture redundancy in volumetric anatomical images. Results show that the proposed method is more efficient than state of the art encoders
dc.subjectsuch as HEVC
dc.subjectby about 28.8%
dc.subjectand achieves a gain of up to 57.8% in compression ratio when compared with traditional methods.
dc.titleContributions to lossless coding of medical images using minimum rate predictorseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50008%2F2020/PT
oaire.citation.conferenceDate2015-09
oaire.citation.conferencePlaceQuebec City, QC, Canada
oaire.citation.title2015 IEEE International Conference on Image Processing (ICIP)
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGuarda
person.familyNameM. M. Rodrigues
person.familyNameFaria
person.givenNameAndré
person.givenNameNuno
person.givenNameSergio
person.identifier.ciencia-idF811-146F-4EE9
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0001-5996-1074
person.identifier.orcid0000-0001-9536-1017
person.identifier.orcid0000-0002-0993-9124
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id7006052345
person.identifier.scopus-author-id14027853900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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