Publication
ROI -BASED CODING OF BIOMEDICAL IMAGES FOR MACHINE ANALYSIS
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.advisor | Faria, Sérgio Manuel Maciel de | |
| dc.contributor.advisor | Távora, Luís Miguel de Oliveira Pegado de Noronha e | |
| dc.contributor.advisor | Thomaz, Lucas Arrabal | |
| dc.contributor.author | Nicolau, Daniel Filipe da Silva | |
| dc.date.accessioned | 2025-07-25T15:08:27Z | |
| dc.date.available | 2025-07-25T15:08:27Z | |
| dc.date.issued | 2025-03-06 | |
| dc.description | I acknowledge the financial support provided by the Fundação para a Ciência e a Tecnologia (FCT), Portugal under projects CoMBINNe 2022.09914.PTDC (DOI:10.54499/2022.09914.PTDC), Programa Operacional Regional do Centro, and by FCT/MCTES through national funds and when applicable co-funded by EU funds under the project UIDB/EEA/50008/2020 (DOI: 10.54499/UIDB/50008/2020) and LA/P/0109/2020 (DOI: 10.54499/LA/P/0109/2020). | |
| dc.description.abstract | The increasing volume of data acquired and generated daily in the healthcare sector, driven by technological advancements, brings significant benefits to patient diagnosis and research. However, this growth also presents considerable challenges in the analysis and processing of such data. To address these difficulties, computer vision algorithms have emerged as powerful tools, capable of automating repetitive and time-consuming tasks, enabling faster and more accurate processing. At the same time, the growing volume of data places pressure on storage and transmission capabilities, demanding efficient compression methods to minimise its size. In the literature, various approaches are found, primarily divided into two categories: lossy and lossless compression. While lossless methods ensure data integrity, they do not achieve compression rates as high as lossy algorithms. The latter, despite significantly reducing file sizes, introduces distortions that may compromise image quality, affecting the accuracy of automated systems. This dissertation focuses on two main challenges: first, evaluating the impact of image compression on the performance of biomedical computer vision systems, and second, improving compression efficiency without compromising the accuracy of these algorithms. To this end, detection and segmentation models, such as YOLOv8 and SAM, were used to analyse the effect of distortion caused by encoding on the localisation and segmentation of mitochondria in two datasets of electron microscopy images. To enhance model performance at higher compression levels, two methodologies were implemented. The first focuses on domain adaptation, fine-tuning the models to recognise and compensate for distortions introduced by compression, specifically in HEVC/H.265 and VVC/H.266 encoders. The second approach proposes contentaware encoder adaptation, allowing the assignment of different quality levels to selected regions of interest. This method aims to reduce storage and bandwidth requirements without significantly compromising the performance of deep learningbased models. Experimental results demonstrate that region-of-interest-based encoding strategies effectively reduce compression rates while maintaining model accuracy. In particular, the proposed methodologies allowed to achieve an average performance improvement of up to 23.70% for the same bpp range and a data size reduction of up to 74.96%. Additionally, a Pareto-based optimisation algorithm was proposed to determine the most suitable encoding configurations for different standards and models, ensuring a balance between compression efficiency and object detection performance. | eng |
| dc.identifier.tid | 203975030 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/13779 | |
| dc.language.iso | eng | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Imagiologia biomédica | |
| dc.subject | Microscopia eletrónica | |
| dc.subject | Compressão com Perdas | |
| dc.subject | YOLO | |
| dc.subject | SAML | |
| dc.subject | HEVC | |
| dc.subject | VVC | |
| dc.subject | Codificação de Regiões de Interesse | |
| dc.subject | Preservação de Conteúdo | |
| dc.title | ROI -BASED CODING OF BIOMEDICAL IMAGES FOR MACHINE ANALYSIS | |
| dc.type | master thesis | |
| dspace.entity.type | Publication |