JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.8/6682 |
Resumo: | The massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compression scheme designed to encode both computerized tomography (CT) and positron emission tomography (PET). Different techniques, such as image-to-image translation, intra prediction, and inter prediction are used. Redundancies between both image modalities are also investigated. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Two approaches were implemented and evaluated to determine a PET residue that will be compressed along with the original CT. In the first method, the residue resulting from the differences between the original PET and its estimation is encoded, whereas in the second method, the residue is obtained using encoders inter-prediction coding tools. Thus, in alternative to compressing two independent picture modalities, i.e., both images of the original PET-CT pair solely the CT is independently encoded alongside with the PET residue, in the proposed method. Along with the proposed pipeline, a post-processing optimization algorithm that modifies the estimated PET image by altering the contrast and rescaling the image is implemented to maximize the compression efficiency. Four different versions (subsets) of a publicly available PET-CT pair dataset were tested. The first proposed subset was used to demonstrate that the concept developed in this work is capable of surpassing the traditional compression schemes. The obtained results showed gains of up to 8.9% using the HEVC. On the other side, JPEG2k proved not to be the most suitable as it failed to obtain good results, having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains, when compared to conventional compression methods, up 6.33% compression gain using HEVC, and 7.78% using VVC. |
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JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKSComputerized tomographyPositron emission tomographyLossless compressionGenerative Adversarial NetworkImage-to-image translationMedical Image CodingDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaThe massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compression scheme designed to encode both computerized tomography (CT) and positron emission tomography (PET). Different techniques, such as image-to-image translation, intra prediction, and inter prediction are used. Redundancies between both image modalities are also investigated. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Two approaches were implemented and evaluated to determine a PET residue that will be compressed along with the original CT. In the first method, the residue resulting from the differences between the original PET and its estimation is encoded, whereas in the second method, the residue is obtained using encoders inter-prediction coding tools. Thus, in alternative to compressing two independent picture modalities, i.e., both images of the original PET-CT pair solely the CT is independently encoded alongside with the PET residue, in the proposed method. Along with the proposed pipeline, a post-processing optimization algorithm that modifies the estimated PET image by altering the contrast and rescaling the image is implemented to maximize the compression efficiency. Four different versions (subsets) of a publicly available PET-CT pair dataset were tested. The first proposed subset was used to demonstrate that the concept developed in this work is capable of surpassing the traditional compression schemes. The obtained results showed gains of up to 8.9% using the HEVC. On the other side, JPEG2k proved not to be the most suitable as it failed to obtain good results, having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains, when compared to conventional compression methods, up 6.33% compression gain using HEVC, and 7.78% using VVC.Assunção, Pedro António AmadoTávora, Luís Miguel de Oliveira Pegado de Noronha eThomaz, Lucas ArrabalIC-OnlineParracho, João Oliveira2022-02-16T10:53:48Z2020-11-082020-11-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.8/6682TID:202942058enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T15:53:44Zoai:iconline.ipleiria.pt:10400.8/6682Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:49:52.604519Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
title |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
spellingShingle |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS Parracho, João Oliveira Computerized tomography Positron emission tomography Lossless compression Generative Adversarial Network Image-to-image translation Medical Image Coding Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
title_full |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
title_fullStr |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
title_full_unstemmed |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
title_sort |
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS |
author |
Parracho, João Oliveira |
author_facet |
Parracho, João Oliveira |
author_role |
author |
dc.contributor.none.fl_str_mv |
Assunção, Pedro António Amado Távora, Luís Miguel de Oliveira Pegado de Noronha e Thomaz, Lucas Arrabal IC-Online |
dc.contributor.author.fl_str_mv |
Parracho, João Oliveira |
dc.subject.por.fl_str_mv |
Computerized tomography Positron emission tomography Lossless compression Generative Adversarial Network Image-to-image translation Medical Image Coding Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Computerized tomography Positron emission tomography Lossless compression Generative Adversarial Network Image-to-image translation Medical Image Coding Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
The massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compression scheme designed to encode both computerized tomography (CT) and positron emission tomography (PET). Different techniques, such as image-to-image translation, intra prediction, and inter prediction are used. Redundancies between both image modalities are also investigated. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Two approaches were implemented and evaluated to determine a PET residue that will be compressed along with the original CT. In the first method, the residue resulting from the differences between the original PET and its estimation is encoded, whereas in the second method, the residue is obtained using encoders inter-prediction coding tools. Thus, in alternative to compressing two independent picture modalities, i.e., both images of the original PET-CT pair solely the CT is independently encoded alongside with the PET residue, in the proposed method. Along with the proposed pipeline, a post-processing optimization algorithm that modifies the estimated PET image by altering the contrast and rescaling the image is implemented to maximize the compression efficiency. Four different versions (subsets) of a publicly available PET-CT pair dataset were tested. The first proposed subset was used to demonstrate that the concept developed in this work is capable of surpassing the traditional compression schemes. The obtained results showed gains of up to 8.9% using the HEVC. On the other side, JPEG2k proved not to be the most suitable as it failed to obtain good results, having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains, when compared to conventional compression methods, up 6.33% compression gain using HEVC, and 7.78% using VVC. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-08 2020-11-08T00:00:00Z 2022-02-16T10:53:48Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.8/6682 TID:202942058 |
url |
http://hdl.handle.net/10400.8/6682 |
identifier_str_mv |
TID:202942058 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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