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LOSSY COMPRESSION OF BIOMEDICAL IMAGES FOR COMPUTER VISION ANALYSIS

2024-06-14Master thesis Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapt_PT
dc.contributor.advisorFaria, Sérgio Manuel Maciel de
dc.contributor.advisorTávora, Luís Miguel de Oliveira Pegado de Noronha e
dc.contributor.advisorThomaz, Lucas Arrabal
dc.contributor.authorPaulo, Edgar da Silva
dc.date.accessioned2024-09-10T12:57:27Z
dc.date.available2024-09-10T12:57:27Z
dc.date.issued2024-06-14
dc.description.abstractThe exponential increase in medical and biomedical data acquisition is compelled by technological advances, namely in the imaging field. However, this exponential growth brings with it challenges in terms of processing capacity, transmission, and data storage. In response to this growing demand, increasingly efficient solutions have emerged, especially through computer vision for automatic image analysis and compression algorithms. This dissertation aims, on the one hand, to evaluate the performance of computer vision systems on previously compressed biomedical images. On the other hand, it increases the useful range of image variations, almost lossless and lossy, decreasing the impact of the change added by this method on the performance of computer vision algorithms in biomedical image analysis. In this sense, YOLO and Detectron2 are employed to evaluate the impact of coding distortion on their ability to detect mitochondria in electron microscopy images. The results of this study reveal that although the distortion introduced by compression affects their detection performance, it is negligible at lower compression ratios. Furthermore, two proposals are presented to improve the useful compression ratio, keeping the images characteristics that allow to perform the automatic detection of mitochondria. On the one hand, it is demonstrated that the proposed training methodology, which incorporates compressed versions of the original data during training, mitigates the impact of distortion on the performance of computer vision algorithms; on the other hand, allocating higher quality levels to regions of interest, compared to background elements, helps to sustain high performance at compression rates where computer vision algorithms typically start to lose effectiveness. These approaches allow the extension of the compression range with little impact on detection performance, thus contributing to the improvement of data processing, storage, and transmission in biomedical applications.pt_PT
dc.identifier.tid203692136pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.8/10029
dc.language.isoengpt_PT
dc.relationCompression of Multimodal Biomedical Images using Neural Networks
dc.relationInstituto de Telecomunicações
dc.relationInstitute of Telecommunications
dc.subjectBiomedical Imagespt_PT
dc.subjectElectron Microscopy Imagespt_PT
dc.subjectLossy and near Lossless Compressionpt_PT
dc.subjectYOLOpt_PT
dc.subjectDetectron2pt_PT
dc.subjectHEVCpt_PT
dc.subjectRegion Codingpt_PT
dc.subjectMedical Image Compressionpt_PT
dc.titleLOSSY COMPRESSION OF BIOMEDICAL IMAGES FOR COMPUTER VISION ANALYSISpt_PT
dc.typemaster thesis
dspace.entity.typePublication
oaire.awardTitleCompression of Multimodal Biomedical Images using Neural Networks
oaire.awardTitleInstituto de Telecomunicações
oaire.awardTitleInstitute of Telecommunications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/2022.09914.PTDC/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0109%2F2020/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
relation.isProjectOfPublication5fe22f63-2c55-4683-bae7-89d5dba06107
relation.isProjectOfPublication0836c6a6-afd0-499e-8a16-612dd27ec1dc
relation.isProjectOfPublication43215f6c-bfb6-4829-8e75-6096384ce6db
relation.isProjectOfPublication.latestForDiscovery5fe22f63-2c55-4683-bae7-89d5dba06107
thesis.degree.nameMestrado em Engenharia Electrotécnicapt_PT

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